Seminars

Seminerler

2016-2017

New Seminar !!!İlke Bakır- Methods for Optimization under Uncertainty with Applications in Fleet Management

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 27,Tuesday 13:40
EA 409, Bilkent University
by
İlke Bakır
Georgia Institute of Technology

Abstract:

In recent years, large-scale optimization with emphasis on transportation applications has become a particularly exciting field of research, thanks to the rapid technological advancement in transportation equipment and computational tools. Focusing on previously studied problems with this new perspective, as well as defining and solving new problems inspired by these developments provide valuable insights. This talk addresses modern challenges in transportation and logistics planning from both methodological and practical standpoints. In the first part of this talk, we present a robust optimization model for facilitating empty repositioning of mobile resources in large-scale transportation networks, where demand is uncertain. We extend the "budget of uncertainty" idea to realistically represent empty repositioning settings, and propose a rolling horizon framework featuring this model. We then conduct a comprehensive computational study to demonstrate the effectiveness of the proposed approach. In the second part of this talk, we provide a method for computing dual bounds for multistage stochastic mixed integer programming problems with a finite number of scenarios. These dual bounds, called "partition bounds", are based on solving group subproblems on partitions of the scenario set. We develop a scenario set partition sampling method to obtain effective bounds, and demonstrate the power of this method against another sampling-based approach (Sample Average Approximation) on a wide range of test instances.

New Seminar !!!Murat A. Erdoğdu- Stein’s Lemma and Subsampling in Optimization

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 23,Friday 13:40
EA 409, Bilkent University
by
Murat A. Erdoğdu
Stanford University

Abstract:

Statistics and optimization have been closely linked since the very outset. This connection have become more essential lately, mainly because of the recent advances in computational resources, the availability of large amount of data, and the consequent growing interest in statistical and machine learning algorithms. In the broadest sense, statistical estimation methods have been usually the primary beneficiary of this relation, and in this work we reverse this arrangement. We show that optimization algorithms can also immensely benefit from the classical techniques from statistics such as Stein’s lemma and subsampling. We propose algorithms that have wide applicability to many supervised learning problems such as binary classification with smooth surrogate losses, robust statistics with m-estimators, and generalized linear models. We provide theoretical guarantees of the proposed algorithms, and analyze their convergence behavior in terms of data dimensions. Finally, we demonstrate the performance of our algorithms on well-known classification and regression problems, through extensive numerical studies on large-scale datasets, and show that they achieve the highest performance compared to other widely used and specialized algorithms.

New Seminar !!!James Bailey- Strategic Stable Marriage

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 21,Wednesday 13:40
EA 409, Bilkent University
by
James Bailey
Georgia Institute of Technology, Atlanta, GA

Abstract:

We study stable marriage where individuals strategically submit private preference information to a publicly known stable marriage algorithm. We prove that no stable marriage algorithm ensures actual stability at every Nash equilibrium when individuals are strategic. More specifically, we show that any rational marriage, stable or otherwise, can be obtained at a Nash equilibrium. Thus the set of Nash equilibria provides no predictive value nor guidance for mechanism design. We propose the following new minimal dishonesty equilibrium refinement, supported by experimental economics results: an individual will not strategically submit preference list L if there exists a more honest L’ that yields as preferred an outcome. Then for all marriage algorithms satisfying monotonicity and IIa, every minimally dishonest equilibrium yields a sincerely stable marriage. This result supports the use of algorithms less biased than the (Gale-Shapley) man-optimal, which we prove yields the woman-optimal marriage in every minimally dishonest equilibrium. However, bias cannot be totally eliminated, in the sense that no monotonic IIA stable marriage algorithm is certain to yield the egalitarian-optimal marriage in a minimally dishonest equilibrium- thus answering a 17-year old open question and Irving’s in the negative. Finally, we show that these results extend to student placement problems, where women are polygamous and honest, but not to admissions problems, where women are both polygamous and strategic. If time permits, I will survey my related results on strategic voting and facility location. Based on joint work with Craig Tovey at Georgia Tech.

<> Barış Yıldız-A New Formulation Approach for Vehicle Routing Problem with Backhauls

DEPARTMENT OF INDUSTRIAL ENGINEERING

November 4,Friday 13:40
EA 409, Bilkent University
by
Barış Yıldız
Department of Industrial Engineering , Bilkent University

Abstract:

Due to their practical importance and interesting theoretical properties vehicle routing problems (VRP) constitute one of the major research areas in operations research literature. Various formulations and solution algorithms have been proposed to solve these challenging problems. Motivated by a practical problem that arises in air-cargo delivery we focus on a specific generalization of VRP: vehicle routing problem with backhauls, and propose a novel path-segment formulation and an efficient branch-and-price algorithm to solve it. Using path-segments to represent different stages of transportation, linehaul and backhaul stages, our formulation can very easily handle multiple vehicle types and operational constraints such as vehicle capacity, customs regulations, total delivery/pickup time, etc. We show that the optimal solution of the problem is always a tree in the closure graph of the transportation network and using this property we suggest valid cuts that strengthen our formulation and speed up the branch-and-price algorithm. Our initial computational studies show that our solution approach can significantly extend the size of the problems that can be solved exactly.

Kerem Uğurlu - Decomposability and time consistency of risk averse multistage programs

DEPARTMENT OF INDUSTRIAL ENGINEERING

October 26, 2016, Wednesday 11:00
EA 409, Bilkent University
by
Kerem Uğurlu
Department of Industrial Engineering , Bilkent University

Abstract:

Two approaches to time consistency of risk averse multistage stochastic problems were discussed in the recent literature. In one approach certain properties of the corresponding risk measure are postulated which imply its decomposability. The other approach deals directly with conditional optimality of solutions of the considered problem. The aim of this talk is to discuss a relation between these two approaches and discuss some open questions related to it. Part of the talk is joint work with Alexander Shapiro.

Savaş Dayanık - Bidding for multiple keywords in search-based advertising

DEPARTMENT OF INDUSTRIAL ENGINEERING

October 21, 2016, Friday 13:40
EA 409, Bilkent University
by
Savaş Dayanık
Department of Industrial Engineering , Bilkent University

Abstract:

Search-based advertisement allows small-to-medium size companies to use more effectively their advertisement budgets to target their potential customers. The advertisers manage this by bidding for a place on the result pages for several search keywords relevant to their products and services. The customer flow rates to the advertiser business pages change with the placements of advertisements on the pages, which are determined by the bidding prices of the advertisers in closed second-price auctions. We model the bidding process and calculate optimal dynamic bidding prices for several keywords subject to a single daily advertisement budget. (This is joint work with Semih Onur Sezer, Sabancı University)

Nesim K. Erkip - Teaching Experience with Freshman Students – An Overview of IE102

DEPARTMENT OF INDUSTRIAL ENGINEERING

October 7, 2016, Friday 13:40
EA 409, Bilkent University
by
Nesim K. Erkip
Department of Industrial Engineering , Bilkent University

Abstract:

IE102, A Process Oriented Outlook for Industrial Engineering, is a freshman course that became part of Bilkent University IE Curriculum starting with Spring Semester, 2010. In this talk, I want to discuss the approach I followed in teaching IE102. The talk can be considered as a follow-up of a seminar I conducted in April 2012, entitled, “Teaching Experience with Freshman Students – a Premature Evaluation of IE102 Course” . The talk will start historical anecdotes of discussions that took place before establishing the course. Most of the discussions were focused on the students’ abstraction skill, which was seen as a key issue for the success of graduates. The course has slightly changed over the years, mostly in the way that students are assessed, and of course with refinements in the way topics are covered. The talk aims to share my experiences with the course, as well as my self-assessment of its performance. Objectives of the course and accompanying material covered in the course are detailed, followed by the description of the weekly program. Explanation on how assessment has been carried out, together with some the approach considered for course conduct will be presented. Some hints on how to keep student concentration alive in the classroom, as well as some methods used to create student interaction will be explained. As conclusion, I will attempt to assess the value of the course, as well as share some thoughts on improving the course.

2015-2016

New Seminar !!! Mehdi Ghaniabadi - CAPACITATED ASSORTMENT OPTIMIZATION AND PRICING PROBLEMS UNDER MIXED MULTINOMIAL LOGIT MODEL

DEPARTMENT OF INDUSTRIAL ENGINEERING

August 9, 2016, Tuesday 14:00
EA 409, Bilkent University
by
Mehdi Ghaniabadi
Predictive and Economic Modeling , Merck Research Labs

Abstract:

We study capacitated assortment optimization problem under mixed multinomial logit model where a retailer wants to choose the set of products to offer to various customer segments with the goal of maximizing revenue while satisfying different capacity constraints. Each customer segment is identified with a unique purchase behaviour modelled by multinomial logit demand. We consider three general cases of capacity constraints: single resource constraint, multiple resource constraints and multiple cardinality constraints. This problem is NP-hard and there exist two approaches to find exact solutions: formulating the problem as a mixed integer linear program (MILP) or a mixed integer conic quadratic program (CONIC). For each constraint structure, we develop new efficient procedures to derive McCormick valid inequalities. We provide extensive numerical studies the results of which demonstrate that when the CONIC model is accompanied with the McCormick inequalities, the problem can be solved effectively even for large sized instances using a commercial optimization software. We also study joint pricing and assortment optimization problem with a single cardinality constraint and establish a new procedure to construct McCormick inequalities. We then present the related numerical studies which indicate that the CONIC formulation accomplishes the best outcome in the presence of the McCormick inequalities.

Murat Kurt - Valuing Prearranged Paired Kidney Exchanges: A Stochastic Game Approach

DEPARTMENT OF INDUSTRIAL ENGINEERING

July 18, 2016, Monday 11:00
EA 409, Bilkent University
by
Murat Kurt
Predictive and Economic Modeling , Merck Research Labs



Abstract: End-stage renal disease (ESRD) is the ninth-leading cause of death in the U.S. Transplantation is the most viable therapy for ESRD patients, but there is a severe disparity between the demand for kidneys for transplantation and the supply. This shortage is further complicated by incompatibilities in blood-type and antigen matching between patient-donor pairs. Paired kidney exchange (PKE), a cross-exchange of kidneys among incompatible patient-donor pairs, overcomes many difficulties in matching patients with incompatible donors. PKEs have grown rapidly over the last two decades. In a PKE, transplantation surgeries take place simultaneously, so that no donor may renege after his/her intended recipient receives the organ. Although others have modeled PKEs, we are the first to consider patient autonomy and the timing of the exchange. As current PKE practice aims to maximize only the number of transplants, the question of determining a more accurate value of a match remains unclear. We consider a cyclic PKE with an arbitrary number of patients and construct life-expectancy-based edge weights under patient autonomy. Because the patients' health statuses are dynamic, and transplantation surgeries require compatibility between the patients’ willingnesses to exchange, we model the patients’ transplant timing decisions as a stochastic game in which each patient aims to maximize his/her life expectancy. We explore necessary and sufficient conditions for patients' decisions to be a Nash equilibrium, and formulate a mixed-integer linear programming representation of equilibrium constraints, which provides a characterization of the socially optimal equilibria. We empirically confirm that randomized strategies do not yield a social welfare gain over pure strategies. We also quantify the social welfare loss due to patient autonomy and demonstrate that maximizing the number of transplants may be undesirable. Our results highlight the importance of the timing of an exchange and disease severity on matching patient-donor pairs. This is a joint work with Andrew Schaefer from Rice University, Utku Unver from Boston College and Mark Roberts from University of Pittsburgh. Bio: Murat Kurt is a Scientist of Outcomes Research in Predictive and Economic Modeling at Merck Research Labs. He earned his PhD in Industrial Engineering at the University of Pittsburgh and served as a faculty in Industrial and Systems Engineering at the University at Buffalo prior to joining Merck. His research interests involve the implementation of Markov decision processes and mathematical programming on medical decision making, scheduling and service operations problems. His research in applied health sciences span varying contexts from treatment planning for Type 2 diabetes and colorectal cancer patients to the timing of kidney exchanges for end-stage renal disease patients. His research on healthcare applications has been recognized by the IIE's Pritsker Doctoral Dissertation Prize and INFORMS Service Science Section's Best Paper Awards as well as Seth Bonder Scholarship in Applied Health Sciences.

Ahmed Burak Paç - ROBUST PORTFOLIO OPTIMIZATION WITH RISK MEASURES UNDER DISTRIBUTIONAL UNCERTAINTY

DEPARTMENT OF INDUSTRIAL ENGINEERING

July 12, 2016, Tuesday 16:00
EA 409, Bilkent University
by
Ahmed Burak Paç
Bilkent University, Department of Industrial Engineering

Title: ROBUST PORTFOLIO OPTIMIZATION WITH RISK MEASURES UNDER DISTRIBUTIONAL UNCERTAINTY

Abstract:

Abstract: In this study, we consider the portfolio selection problem with different risk measures and different perspectives regarding distributional uncertainty. First, we consider the problem of optimal portfolio choice using the first and second lower partial moment risk measures, for a market consisting of n risky assets and a riskless asset, with short positions allowed. We derive closed-form robust portfolio rules minimizing the worst case risk measure under uncertainty of the return distribution given the mean/covariance information. A criticism levelled against distributionally robust portfolios is sensitivity to uncertainties or estimation errors in the mean return data, i.e., Mean Return Ambiguity. Modeling ambiguity in mean return via an ellipsoidal set, we derive results for a setting with mean return and distributional uncertainty combined. Using the adjustable robustness paradigm we extend the single period results to multiple periods in discrete time, and derive closed-form dynamic portfolio policies. Next, we consider the problem of optimal portfolio choice minimizing the CVaR and VaR measures under the minimum expected return constraint. We derive the optimal portfolio rules for the ellipsoidal mean return vector and distributional ambiguity setting. In the presence of a riskless asset, the robust CVaR and VaR measures, coupled with a minimum mean return constraint, yield simple, mean-variance efficient optimal portfolio rules. In a market without the riskless asset, we obtain a closed-form portfolio rule that generalizes earlier results, without a minimum mean return restriction. In the final problem, we have a change of perspective regarding uncertainty. Rather than the information on first and second moments, knowledge of a nominal distribution of asset returns is assumed, and the actual distribution is considered to be within a ball around this nominal distribution. The metric choice on the probability space is the Kantorovich distance. We investigate convergence of the risky investment to uniform portfolio when a riskless asset is available. While uniform investment to risky assets becomes optimal, it is shown that as the uncertainty radius increases, the total allocation to risky assets diminishes. Hence, as uncertainty increases, the risk averse investor is driven out of the risky market.

Burhaneddin Sandıkçı - The role of waiting list information in liver transplant decision making

DEPARTMENT OF INDUSTRIAL ENGINEERING

June 21, 2016, Tuesday 13:40
EA 409, Bilkent University
by
Burhaneddin Sandıkçı
University of Chicago Booth School of Business, USA


Abstract:

In the United States, patients in need of a liver transplant receive deceased-donor organ offers through joining a waiting list. Accepting or rejecting an offered organ is largely influenced by the patient’s prospects for future offers, which can be ascertained most accurately by knowing the entire composition of the waiting list. We present two stochastic models to help individual patients make optimal accept/reject decisions when faced with an offer. The first model assumes perfect information about the composition of waiting list, whereas the second model contends with partially observable waiting list as available in the current US system. In addition to modeling novelties, we present structural analyses of these models to characterize the optimal decision rule and a detailed numerical study investigating the impact of waiting list information on patients’ life expectancies. We find a significant loss in a patient’s life expectancy, on average, when the patient ignores the waiting list information, and that the currently published partial information is nearly sufficient to eliminate this loss.

Bio: Burhaneddin Sandıkçı is an Associate Professor of Operations Management at the University of Chicago Booth School of Business. He received his PhD in industrial engineering in 2008 from the University of Pittsburgh. His prior training includes an MS in operations research (University of North Carolina at Chapel Hill), an MS in industrial engineering (Bilkent University), and a BS in industrial egineering (Marmara University). His research interests span decision-making problems under uncertainty with particular focus on problems in medical decision-making and healthcare operations. His methodological interests include Markov decision processes (MDPs), partially observed MDPs, stochastic programming, and simulation. His research has been published in leading academic journals such as Operations Research, Management Science, and Mathematical Programming. His work has also been recognized at various levels by INFORMS Decision Analysis Society, INFORMS Bonder Scholarship, and IIE Pritsker Doctoral Dissertation Award.

Atalay Atasu - Design Incentives under Extended Producer Responsibility: A Network Perspective

DEPARTMENT OF INDUSTRIAL ENGINEERING

June 15, 2016, Wednesday 13:40
EA 409, Bilkent University
by
Atalay Atasu
Georgia Tech - Scheller College of Business,
USA,
Operations Management


Abstract:

A key goal of Extended Producer Responsibility (EPR) legislation is to provide incentives for producers to design their products for recyclability. EPR is typically implemented in a collective system, where a network of recycling resources are coordinated to fulfill the EPR obligations of a set of producers, and the resulting system cost is allocated among these producers. Collective EPR is prevalent because of its cost efficiency advantages. However, it is considered to provide inferior design incentives compared to an individual implementation (where producers fulfill their EPR obligations individually). In this paper, we revisit this assertion and investigate its fundamental underpinnings in a network setting. To this end, we develop a new biform game framework that captures producers' independent design choices (non-cooperative stage) and recognizes the need to maintain the voluntary participation of producers for the collective system to be stable (cooperative stage). This biform game subsumes the network-based operations of a collective system and captures the interdependence between producers' product design and participation decisions. We characterize the manner in which design improvement may compromise stability and vice versa. Yet we establish that a stable collective EPR implementation can match and even surpass an individual implementation with respect to product design outcomes. Our analysis uncovers network properties that can be exploited to develop cost allocations that achieve such superior design outcomes.

Bio: Atalay Atasu, PhD (INSEAD, 2007), is associate professor of operations management at the Georgia Tech Scheller College of Business. His research expertise is on sustainable operations management, with focus on product recovery economics and extended producer responsibility, on which he has published extensively. His research appeared in Management Science, Manufacturing & Service Operations Management, Production and Operations Management, Journal of Industrial Ecology, and California Management Review. He is the recipient of a number of research awards, including the Wickham Skinner Best Paper Award (winner 2007, runner up 2014), Wickham Skinner Early Career Research Award (2012), and Paul Kleindorfer Award in Sustainability (2013). His research originating from extensive collaborations with a number of Electronics Manufacturers in Europe, particularly in the context of extended producer responsibility, has been particularly influential in the European WEEE Directive implementations.

Murat Fadıloğlu - A Linear Programming Framework for the Control of Tandem Production Lines

DEPARTMENT OF INDUSTRIAL ENGINEERING

May 31, 2016, Tuesday 11:00
EA 409, Bilkent University
by
Murat Fadıloğlu
Yaşar University
Department of Industrial Engineering


Abstract:

We consider the problem of production control and stock rationing in a make-to- stock production system with several customer classes that generate independent Poisson demands. The system consists of a tandem production line with a finished goods buffer at the end. The tandem line is composed of reliable/unreliable workstations with exponential production times and intermediate buffers. At decision epochs, in conjunction with the stock allocation decision for the finished goods, the control specifies whether to continue or stop production at each workstation. We model the system as a Markov Decision Process and then propose an equivalent linear programming formulation. This formulation enables us to solve larger models in reasonable times by harnessing the power of the state-of-art optimization solvers. Furthermore, this approach provides us with the flexibility to use our MDP model to find the optimal parameters to well-known control policies such as Kanban.

Bio: Dr. Murat Fadıloğlu is currently professor of industrial engineering at Yaşar University. He received his B.S. degree in electrical engineering from Bilkent University. He completed his doctoral studies at the Department of Industrial and Systems Engineering of University of Florida. He previously worked at Bilkent University and Izmir University of Economics. His research interests include performance evaluation of inventory/production systems, queueing models, stochastic modeling, dynamic programming, optimal control and supply chain management. He teaches courses on stochastic modeling, simulation and probability theory. Dr. Fadıloğlu is a member of the Institute for Operations Research and the Management Sciences. He is interested in sports (bicycling, running, diving, squash), literature, and world music, cinema and cuisines.

Refik Güllü - Innovation Race under Revenue and Technology Uncertainty where the Winner Does Not Take All

DEPARTMENT OF INDUSTRIAL ENGINEERING

April 29, 2016, Friday 13:40
EA 409, Bilkent University
by
Refik Güllü
Boğaziçi University
Department of Industrial Engineering


Abstract:

We analyze the competitive investment behavior on innovative products or services under revenue and technology uncertainty for heterogeneous firms. Firms make a decision on how much to invest in research and development of an innovative technology at the beginning of the time horizon. They discover the technology at an uncertain time in the future. The time of successful discovery depends on the amount of investment and the characteristics of the firms. All firms collect revenues even though they are not winners. Although there can be positive or negative external shocks, the potential revenue rates decrease in time and the first firm to adopt the technology is less prone to negative shocks and benefits more from positive shocks. Therefore, the competition is a stochastic race, where all firms collect some revenue once they adopt. We show the existence of a pure strategy Nash equilibrium for this game in a duopoly market under general assumptions and provide more structural results when the time to successfully innovate is exponentially distributed. We show the uniqueness of the equilibrium for an arbitrary number of symmetric firms. We argue that for sufficiently efficient firms who are resilient against market shocks, consolidating racing firms will decrease their expected profits. We also provide an illustrative computational analysis for comparative statics, where we show the non-monotonic behavior of equilibrium investment levels as examples.
Joint work with Taner Bilgiç, Boğaziçi University IE Department.
Bio: Refik Güllü is a Professor and Chairperson in the Industrial Engineering Department of Boğaziçi University, Istanbul, Turkey. He received his B.S. and M.S. degrees in Industrial Engineering from the Middle East Technical University, Ankara, Turkey, and M.S. and Ph.D. degrees in Operations Research from the School of ORIE, Cornell University, New York. He worked at the Middle East Technical University as a faculty member before joining Boğaziçi University. His main research interest is stochastic modeling of manufacturing and service systems.

A. Ridha Mahjoub - Structural Analysis for Conditional-Algebraic Systems, and Combinatorial Optimization

DEPARTMENT OF INDUSTRIAL ENGINEERING

April 22, 2016, Friday 13:40
EA 409, Bilkent University
by
A. Ridha Mahjoub
Université Paris-Dauphine


Abstract:

Differential-Algebraic Systems (DAS) are used for modeling complex physical systems such as electrical networks and dynamic movements. Such a system can be given as F(x,x’,t)=0 where x is the variable vector, t the time and x’ the derivative vector associated to x with respect to time. In many practical situations, physical systems may have different states generated by some technical conditions. These may be, for instance, related to temperature change. Such systems generally yield conditional DASs which may have different states depending on the assignment of the true and false values to the conditions. Each assignment yields a non-conditional system called a state of the system. A main problem, which arises in the analysis of DASs, is to know whether or not the system may have a solution in each state, and if not to determine a state in which the system has no solution. This is called the Structural Analysis Problem. In this talk, we consider this problem. We give an integer programming formulation for the problem. This is based on a transformation of the problem into a matching problem in an auxiliary graph. We show that the linear relaxation can be solved in polynomial time. We also describe some valid inequalities. Using this, we will discuss a Branch-and-Cut algorithm for the problem along with some experimental results.
Bio: A. Ridha Mahjoub is Professeur Classe Exceptionnelle of Operations Research and Combinatorial Optimization at Université Paris-Dauphine, Paris, France. He is also member of the LAMSADE laboratory, CNRS. Previous positions include full professor at the University of Brest, France, from 1991 to 1998 and the University of Clermont-Ferrand, France, from 1998 to 2007. Dr. Mahjoub holds an undergraduate degree in Mathematics from University of Tunis, Tunisia and a Ph.D. and a Doctorat d’Etat in Operations Research and Combinatorial Optimization from the University of Grenoble, France. His research areas include the theory and applications of polyhedral approaches for modeling, analyzing and solving large NP-hard combinatorial optimization problems, mixed integer programming as well as complexity and graph theory. A part of his research has recently focused on the design of cutting plane algorithms for network design problems. Dr. Mahjoub is author and co-author of several papers that have appeared in leading journals such as Mathematical Programming, Mathematics of Operations Research, SIAM Journal on Discrete Mathematics, Discrete Mathematics, Discrete Applied Mathematics, Discrete Optimization, Informs Journal on Computing and Networks. He served as Co-Director of the Mathematics and Computer Science Department at Université Paris-Dauphine between 2008 and 2013. Dr. Mahjoub edited and co-edited books and several special issues of journals. He currently serves as Editor-in-Chief of the international journal RAIRO-Operations Research and Area Editor of the journal Computers and Industrial Engineering.

Enis Kayış - A Predictive and Prescriptive Analytics Approach to Next-Day Operating Room Scheduling Problem

DEPARTMENT OF INDUSTRIAL ENGINEERING

April 15, 2016, Friday 13:40
EA 409, Bilkent University
by
Enis Kayış
Bilkent University
Özyeğin University, Department of Industrial Engineering
PhD THESIS ANNOUNCEMENT

Abstract:

Operating room (OR) is one of the most critical and expensive resources in hospitals. OR scheduling has an impact on many key performance criteria for healthcare delivery systems such as resource utilization, patient satisfaction, and staff overtime. However, due to many sources of uncertainties, multiple stakeholders, and complexity of operations, OR planning is a challenging problem and still an open research area.
Data resulting from increasingly widespread deployment of electronic health record (EHR) systems are starting to provide an important foundation to improve operational efficiency using data driven models at hospitals. I will describe an OR planning and scheduling system that predicts procedure duration and prescribes next-day OR plans to meet multiple criteria. First, I will present an adjustment method based on a combination of operational, temporal, and staff related factors that improve traditional surgery duration estimates. Using two years of detailed operational data from an EHR system, we conclude that while improving estimates of surgery durations is possible, the inherent variability in such estimates remains high, necessitating caution in their use when creating OR schedules.
In the second part of the talk, I will present the daily scheduling problem of a single OR with uncertain surgery durations. Our aim is to find the optimum sequence and scheduled starting times of the surgeries to minimize weighted sum of expected patient waiting times and OR idle times. In addition to analytical results providing useful insights on the characteristics of the optimum solutions, we develop heuristics for sequencing and duration assignments and compare their performance. Our heuristics provide insights for the practitioners that do not have the resources to implement more advanced models.
Bio: Enis Kayış is an Assistant Professor of Industrial Engineering at Özyeğin University. He received his PhD from Management Science and Engineering from Stanford University in 2009 and his MS in Statistics from the same university in 2007. During 2009-2012, he had worked at Hewlett-Packard Labs as a research scientist in several projects including demand estimation, product portfolio management and pricing, healthcare operations, and forecasting. His current research interests include data-driven decision making and business analytics with applications in outsourcing, supply chain contracting, and healthcare operations. Prior to his graduate studies, he received BS in Industrial Engineering and Mathematics, both from Bogazici University.

Okan Arslan - Energy Management in Plug-in Hybrid Electric Vehicle Penetrated Networks

DEPARTMENT OF INDUSTRIAL ENGINEERING

April 12, 2016, Tuesday 11:00
EA 409, Bilkent University
by
Okan Arslan
Bilkent University
Department of Industrial Engineering
PhD THESIS ANNOUNCEMENT

Abstract:

With the introduction of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) into the transportation system, a new line of research has emerged in the literature that reconsiders existing problems from the electrified transportation point of view. In this context, our objective is to understand the challenges that arise with the emergence of PHEV technology. Due to their ability to use electricity and gasoline as sources of energy with different cost structures and limitations, PHEVs stand as both a challenge and an opportunity for the existing transportation systems. They provide transportation cost reductions by utilizing less gasoline, which in turn contribute to the environmental benefits. In this context, we addressed a practically important problem: 'Finding the minimum cost path for PHEVs'. We formally present this problem, show that it is NP-Complete and propose exact and dynamic programming based heuristic solution techniques. Using these techniques, we investigate impacts of battery characteristics, driver preferences and road network features on travel costs of a PHEV in long-distance trips. Through this analysis, the location of charging stations is identified as one of the critical factors affecting the costs. In this regard, we introduce another practically important problem: 'Hybrid charging station location'. Different than existing approaches to the charging station location problems, we also consider PHEVs when locating stations. We propose a Benders Decomposition algorithm as an exact solution methodology, and accelerate the implementation by generating nondominated cuts. Finally, we analyze the cost and emission impacts of PHEV penetration into electricity networks with widespread adoption of distributed energy resources.
Approaching PHEVs from a long-distance point of view, we introduce new problems and solution approaches to the literature. Our results show that by establishing an adequate level of the intercity charging station infrastructure, well-studied benefits of electrified transportation in urban regions can be extended to long-distance trips.

Ramez Kian - PRODUCTION DECISIONS WITH CONVEX COSTS AND CARBON EMISSION CONSTRAINTS

DEPARTMENT OF INDUSTRIAL ENGINEERING

March 23, 2016, Wednesday 11:00
EA 409, Bilkent University
by
Ramez Kian
Bilkent University
Department of Industrial Engineering
PhD THESIS ANNOUNCEMENT

Abstract:

In this study, different variants of the production planning problem are considered.
We first study an uncapacitated deterministic lot sizing model with a special production function which determines the relation between resource and output quantities. In particular, we have considered the Cobb-Douglas production function which is applied in sectors such as energy, agriculture and cement industry. We demonstrate that this problem can be reformulated as a lot sizing problem with nonlinear production cost which is convex under certain assumptions. We develop a polynomial time dynamic programming based algorithm and several fast heuristics to solve the model. We compare the performances of the heuristics with extensive numerical tests.
Next, motivated from the reformulation of the first problem, we consider a lot sizing problem with convex nonlinear production and holding costs for decaying items. The problem is investigated from mathematical programming perspective. We propose a structural procedure to reformulate the problem in the form of second order cone programming and employ some optimality and valid cuts to strengthen the model. We conduct an extensive computational test to see the effect of cuts in different formulations. We also study the performance of the heuristics in a rolling horizon basis. We conduct an extensive numerical study to compare the heuristics and to see the effect of forecast horizon length on heuristic dominance order and to see where they outperform exact solution approaches. Finally, we study the lot sizing problem with carbon emission constraints. We propose two Lagrangian heuristics when emission constraint is cumulative over periods. We extend the model with possibility of lost sales and examine several carbon cap policies for a cost minimizer manufacturer. We conduct a cost-emission Pareto analysis for different emission cap configurations.

Hande Dilek - Retail Location Competition under Carbon Penalty

DEPARTMENT OF INDUSTRIAL ENGINEERING

March 22, 2016, Tuesday 09:00
EA 202, Bilkent University
by
Hande Dilek
Bilkent University
Department of Industrial Engineering
MS THESIS ANNOUNCEMENT

Abstract:

This thesis examines the retail location problem on a Hotelling line in two different settings: a decentralized system in which two competing retailers simultaneously choose the locations of their own stores, and a centralized system in which a single retail chain chooses the locations of its two stores. In both settings, the stores procure their products from a common warehouse and each consumer purchases from the closest store. The retailers in the decentralized system want to maximize their individual profits determined by the sales revenue minus the storage cost and the transportation costs for replenishment and consumer travels. The retail chain in the centralized system wants to maximize the sum of the two individual profits. Transportation and storage costs depend on not only fuel and energy consumption but also carbon emission. In the decentralized system with deterministic demand, we establish that both retailers choose the same location in equilibrium at high margin markets. Numerical experiments provide further insights into the location problem: The retail chain chooses different locations for its stores at optimality in all instances. However, under low transportation costs, the retailers in the decentralized system choose the same location in equilibrium. As the consumer transportation costs increase, the stores are located further away from each other towards their respective consumer segments, converging to the centralized solution. The total amount of carbon emissions is always greater under competition due to high emission from consumer travels: Carbon penalty is more effective for consumer travels than for replenishment in reducing emissions.

Fikri Karaesmen - Financial Hedging of Various Inventory Operations

DEPARTMENT OF INDUSTRIAL ENGINEERING

March 18, 2016, Friday 13:40
EA 409, Bilkent University
by
Fikri Karaesmen
Koç University
Department of Industrial Engineering

Abstract:

Inventory operations involve risk due to uncertain demand and supply conditions, fluctuating prices and other factors. Inventory theory mostly deals with this risk in a risk-neutral framework by focusing on expected cost minimization. However, a joint investment in a financial portfolio of instruments can lead to reductions in other risk measures such as the variance of the profit. We propose formulations and approaches for effectively hedging inventory risks using financial instruments. The inventory models range from a newsvendor with demand and supply uncertainty, to multiple customer class revenue management and finally to dynamic multi-period problems involving both price and demand uncertainty.
This is a joint work with S. Özekici, K. Okyay, F. Sayın, G. Sarı and C. Canyakmaz.

Bio: Fikri Karaesmen is a Professor of Industrial Engineering and the Dean of College of Engineering at Koç University. He has a B.S. degree from METU and a Ph.D. from Northeastern University. His research interests are in stochastic models of inventory and service systems. His papers have appeared in Operations Research, Management Science, Manufacturing and Service Operations Management, IIE Transactions and several other journals.

Barış Yıldız - Relay Location in Telecommunications and Transportation Networks

DEPARTMENT OF INDUSTRIAL ENGINEERING
PhD. THESIS ANNOUNCEMENT

March 9, 2016, Wednesday 17:45
EA 409, Bilkent University
by
Barış Yıldız
Bilkent University
Department of Industrial Engineering

Abstract:

With different names and functions, relays play a crucial role in the design of telecommunications and transportation networks and finding optimal relay locations is an important concern in various applications. We investigate several relay location problems from the literature, propose new ones and design efficient solution methods to obtain managerial insights. The basic problem we investigate in this dissertation is the Regenerator Location Problem (RLP). RLP seeks to place the minimum number of regenerators in an optical network so as to facilitate the communication of a signal between any node pair. We revisit RLP from the hub location perspective and introduce two new dimensions involving the challenges of survivability. Considering the flexible optical network architecture, we relax all pairs connectivity, infinite capacity links and single modulation level assumptions of RLP and introduce the regenerator location problem in flexible optical networks (RLP-FON). RLP FON solves regenerator location, routing, bandwidth allocation and modulation selection problems jointly to better exploit the opportunities offered by this novel network architecture. For various problems arising in telecommunications and transportation it is beneficial to consider edge design and relay locations together. We add the edge design aspect to RLP and extend our research to Network Design Problem with Relays. Different than telecommunications networks, the total length of a route is an important issue in transportation. So in the final part we include circuitry constraints to the routing decisions and study the Refueling Station Location Problem for Alternative Fuel Vehicles.
We approach relay location problems from different angles: network topologies, capacities, costs, and demands and provide significant theoretical results. For all relay location problems, the reach limitations for the related entities pose the main challenge and we propose novel path-segment based formulation approaches to incorporate these constraints in an efficient way. Extensive numerical experiments with realistic problem instances attest to the efficacy of the proposed formulation approach.

Özlem Karsu - Handling Equitable Preferences

DEPARTMENT OF INDUSTRIAL ENGINEERING

March 4, 2016, Friday 13:40
EA 409, Bilkent University
by
Özlem Karsu


Abstract:

In this talk we are going to consider a problem that social planners face: evaluating a set of distributions (of income, of wealth, of health, of service levels) across a population, in which individuals are considered preferentially indistinguishable. The social planner’s aim could be to choose the “best” distribution in a given set or rank the distributions.
We use a natural dominance relation, generalized Lorenz dominance, drawing on established equity axioms from economic theory. In some settings there may be additional information about preferences (for example, if there is policy statement that one distribution is preferred to another) and any dominance relation should respect such preferences. However, characterising this sort of conditional dominance relation turns out to be computationally challenging. We present theoretical results that help deal with these challenges and present tractable linear programming formulations for testing whether dominance holds between any given pair of distributions. We also propose an interactive decision support procedure for ranking a given set of distributions. We demonstrate the performance of the approach through computational testing.
This is a joint work with Dr. Alec Morton and Dr. Nikos Argyris.

Bio: Özlem Karsu is an Assistant Professor of Industrial Engineering at Bilkent University. She received her B.S and M.S. degrees from the Industrial Engineering Department of the Middle East Technical University, in 2008 and 2010, respectively. She received her PhD degree in Operational Research from the London School of Economics in 2014. Her research interests include inequity-averse decisions, interactive multi-criteria decision making approaches and various applications of multi-objective optimization.

İlker Birbil - Scalable Distributed Tensor Factorizations with Incremental Quadratic Approximations

DEPARTMENT OF INDUSTRIAL ENGINEERING

February 26, 2016, Friday 13:40
EA 409, Bilkent University
by
İlker Birbil
Sabancı University


Abstract:

We propose HAMSI, a provably convergent incremental algorithm for solving large-scale partially separable optimization problems that frequently emerge in machine learning and inferential statistics. The algorithm is based on a local quadratic approximation and hence allows incorporating a second order curvature information to speed-up the convergence. Furthermore, HAMSI needs almost no tuning, and it is scalable as well as easily parallelizable. In large-scale simulation studies, we illustrate that the method is superior to a state-of-the-art distributed stochastic gradient descent method in terms of convergence behavior. This performance gain comes at the expense of using memory that scales only linearly with the total size of the optimization variables. We conclude that HAMSI may be considered as a viable alternative in many scenarios, where first order methods based on variants of stochastic gradient descent are applicable.

Bio: Ş. İlker Birbil received his PhD degree from North Carolina State University, Raleigh, USA. He then worked for two years as a postdoctoral research fellow in the Netherlands. His research interests include large-scale local and global nonlinear optimization with particular emphasis on algorithm development. He is an associate editor of Journal of Industrial and Management Optimization (JIMO). He is also the cofounder of the website www.bolbilim.com, where he writes mostly about his academic life in Turkey. Currently, he is working as a faculty member in Sabancı University, Istanbul, where he teaches various courses on mathematical programming.

Ayşe Kocabıyıkoğlu - Newsvendor Decisions: The Value of Information

DEPARTMENT OF INDUSTRIAL ENGINEERING

February 19, 2016, Friday 13:40
EA 409, Bilkent University
by
Ayşe Kocabıyıkoğlu
Bilkent University


Abstract:

We provide an experimental study of newsvendor ordering behavior, when information available to the decision makers under the traditional newsvendor setting, such as price and cost levels, and the underlying demand distribution, are not known. We assess subjects’ decisions with respect to the normative, i.e., expected profit maximizing, order levels, total profits generated, and the mismatch cost, i.e., the cost of uncertainty, incurred. Our results show that, although order decisions deviate significantly from expected profit maximizing levels under all conditions, they move closer to normative benchmarks when the underlying demand distribution is known. Having information about price and/or cost levels, on the other hand, does not improve decisions, and may lead to orders significantly farther away from the normative orders compared to when this information is not available. Our studies show decision makers generate significantly higher profits when cost and price levels are not known, compared to when decision makers have full information. We observe that mismatch costs incurred are significantly lower when information about the demand distribution is available to the decision makers. We also explore several possible drivers of the behavior observed in our studies, such as loss aversion, anchoring and adjustment, and demand chasing.

Bio: Ayşe Kocabıyıkoğlu is an Assistant Professor of Decision Sciences at Bilkent University in Ankara, Turkey, and holds a PhD in Decision Sciences from INSEAD, and MS and BS degrees from Middle East Technical University. Her research is at the interface of decision sciences and behavioral operations, and her research interests include behavioral operations management, pricing and revenue management, and dynamic decision making. Her work has featured in leading academic journals such as Management Science, Operations Research and Production & Operations Management.

Athanasia Manou - Strategic Behavior in Transportation Systems

DEPARTMENT OF INDUSTRIAL ENGINEERING

February 12, 2016, Friday 13:40
EA 409, Bilkent University
by
Athanasia Manou
Koç University


Abstract:

Before 1969, queueing theorists limited themselves to recommendations of admin- istrative measures for the reduction of queues. Namely, they assumed that arriving customers would always join a queueing system even if their expected waiting time was large. However, a more realistic assumption is that customers are willing to receive high-quality service and, on the other hand, they are not willing to wait for a long time in the queue. Moreover, customers are free to make decisions about their actions in the system, having as objective the maximization of their own benefit or the social benefit. The study of queueing systems with customers that have the option to make decisions was initiated by Naor (1969). After this work, an emerging tendency to study the behavior of customers that have the option to make decisions took place.

In this talk we present the behavior of strategic customers in transportation systems. We consider two different models. In the first model, arriving customers decide whether to join the station or balk, based on a natural reward-cost structure. Solving the game among customers, we determine their strategic behavior and explore the effect of key service parameters on cus- tomer behavior. In the second model, arriving customers decide whether to join the station or balk and the administrator sets the fee. In this case, a two-stage game among the customers and the administrator takes place. Moreover, we consider three cases distinguished by the level of delay information provided to customers at their arrival instants. We explore how system parameters affect the customer behavior and the fee imposed by the administrator. We then compare the three cases and show that the customers almost always prefer to know their exact waiting times whereas the administrator prefers to provide either no information or the exact waiting time depending on the system parameters.

Bio: Dr. Athanasia Manou is currently a postdoctoral research fellow at department of Indus- trial Engineering, Koç University, Turkey. She received the B.Sc. degree in Mathematics (2006), the M.Sc. degree in Operations Research (2009) and Ph.D. degree in Operations Research (De- cember, 2014) all of them from National and Kapodistrian University, Athens, Greece. Her research interests lie in the areas of Operations Research and Stochastic Processes. In partic- ular, her main interests concern optimization and game theoretic aspects of stochastic systems with applications mainly in queueing systems. In 2014, she received the AXA Postdoctoral Re- search Fellowship award for a two-year project with topic ‘Strategic Customers in Public-Private Service Systems Subject to Uncertainty and Congestion: Equilibrium Analysis’.

Sinan Bayraktar - Hub Location and Routing Problem

DEPARTMENT OF INDUSTRIAL ENGINEERING

January 18, 2016, Friday 11:00
EA 202, Bilkent University
by
Sinan Bayraktar
Industrial and Systems Engineering, Bilkent University

AN M.S. THESIS PRESENTATION

Abstract:

Hubs are special facilities that consolidate and disseminate flows in many-to-many distribution systems. The hub location problem aims to find locations of hubs and allocate non-hub nodes directly to the hubs. However, this problem is necessary to extend when nodes do not have sufficient demand to justify direct connection between the non-hub nodes to the hubs since such direct connection increases the number of vehicles required and decreases the utilization of vehicles. Hence, it is necessary to construct local tours among the nodes allocated to the same hubs to generate economies of scale and to decrease vehicle costs. Nevertheless, forcing each non-hub node to be visited by a local tour is not the best way to design a many-to-many distribution system. Therefore, in this study two options for each non-hub nodes are given: (i) either it could be visited by a local tour or (ii) it could be directly connected to a hub without an economy of scale. We develop a mixed integer programming formulation and strengthen it with valid inequalities. We also develop three different Benders formulations as exact solution methods. In addition, we develop a hierarchical heuristic with two phases in order to solve large-sized problem instances. We test the performances of our solution methodologies on CAB and TR data sets.

Ayşe Nur Arslan - On the polyhedral structure of a multi-capacity mixing set

DEPARTMENT OF INDUSTRIAL ENGINEERING

January 6, 2016, Wednesday 13:40
EA 409, Bilkent University
by
Ayşe Nur Arslan
Industrial and Systems Engineering, University of Florida


Abstract:

Mixed integer programming is a very powerful modeling and decision making tool. It finds applications in many areas of economic and social impact such as supply chain, energy and healthcare management. Unfortunately, solving mixed integer programming problems is challenging due to the inherent non-convexity of their feasible regions. Therefore there is an ever increasing need to improve our understanding of convex (polyhedral) relaxations of such problems. This understanding can then be used to produce better formulations and cutting-plane schemes that help solve these problems efficiently. In this talk, we study the polyhedral structure of a generalized mixing set that we refer to as the multi capacity mixing set. This set arises as part of the formulation of production planning and logistics problems either naturally or as part of a relaxation. We derive two families of facet-defining inequalities for the set under consideration by lifting mixing inequalities. We discuss the properties of the associated lifting function and show that lifting can be performed efficiently. Our results are analogous to earlier results of 1998 - Marchand and Wolsey

Bio: Ayşe Nur Arslan is a Ph.D. candidate at University of Florida's Industrial and Systems Engineering Department. Her Ph.D. advisors are Dr. Jean-Philippe P. Richard and Dr. Yongpei Guan. She received her bachelor's degree in Industrial Engineering from Bogazici University in 2010. Ayşe Nur's research efforts revolve around developing new theory and methodologies for the efficient solution of mixed integer programming problems both deterministic and stochastic. She is especially interested in polyhedral/cutting-plane approaches and their computational applications. Her Ph.D. thesis develops polyhedral results for production planning and logistics problems and investigates the use of these results in a computational framework.

Tolga Aydınlıyım - Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers' Return Policy Decisions

DEPARTMENT OF INDUSTRIAL ENGINEERING

January 5, 2016, Tuesday 13:40
EA 409, Bilkent University
by
Tolga Aydınlıyım
City University of New York


Abstract:

We study how online retailers' return policy choices are affected by consumers' discount-seeking purchase deferrals, i.e., strategic consumer behavior. We focus mostly on the Internet retailing context, where consumers may not be able to assess the fit between their expectations and products at the time of purchase, thus making the issue of how lenient the retailer's return policy paramount. In this research, we investigate whether allowing returns may entice strategic consumers to purchase earlier at full price, as well as how lenient (measured by the magnitude of the refund) the optimal return policy should be. We argue that an effective return policy can not only induce higher full-price sales by limiting regret from an unfit purchase, but also signal a higher chance of clearance period unavailability. However, our results highlight that permitting returns when facing strategic consumers is a double-edged sword, for both a monopolist and competing retailers. We find that, only when a retailer can salvage without a heavy discount, an optimally determined (partial) refund that is less than the clearance period price can mitigate purchase deferrals. This finding contrasts earlier research on returns management for a monopolist retailer, which ignored strategic consumer behavior and found a partial refund that equals the salvage price to be always optimal. We also offer three explanations for when the common retail practice of offering a full refund sustains as an equilibrium decision for a monopolist: (i) Some products can be salvaged without a loss, (ii) a lenient return policy might stimulate additional aggregate demand, (iii) consumers' transaction cost may reduce return volumes. Our analysis of an arbitrary number of competing retailers yields a unique rational expectations equilibrium, where each retailer's equilibrium refund is non decreasing in its unit salvage revenue, which we use to highlight that retailers whose unit salvage revenues are below a threshold should not allow returns, and for the retailers who should optimally offer a refund, a higher salvage revenue implies a higher full price, a higher stocking quantity, and thus a higher profit. Consequently, choosing a clearance sales partner who can salvage at prices higher than competing clearance mediums helps a retailer gain competitive advantage when selling to strategic consumers.

Bio: Tolga Aydinliyim is an Assistant Professor of Operations Management at the Zicklin School of Business, Baruch College, City University of New York. Previously, he was an Assistant Professor of Decision Sciences at the Lundquist College of Business, University of Oregon. He earned a Ph.D. in Operations Research from Case Western Reserve University in 2007, and a B.S. in Industrial Engineering from the Middle East Technical University in 2003. Tolga’s research interests are broadly in operations and supply chain management, with particular focus in two interface areas: capacity allocation issues at the interface of production planning and supply chain coordination; and consumer-driven demand considerations at the operations-marketing interface with particular focus on retail operations. His papers have appeared in Manufacturing & Service Operations Management, Decision Sciences, and other operations management journals and books.

Sercan Yildiz - A Cutting-Surface Theory for Mixed-Integer Conic Programs

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 25, 2015, Friday 13:40
EA 409, Bilkent University
by
Sercan Yildiz
Carnegie Mellon University


Abstract:

Mixed-integer conic programming (MICP) is a natural generalization of conic programming and mixed-integer linear programming (MILP) where a linear objective function is optimized over the intersection of a regular--possibly nonlinear--cone with an affine subspace subject to integrality constraints. The combined modeling power of integer variables and conic constraints makes MICP an attractive modeling framework for optimization under uncertainty as well as engineering design and statistical learning. However, the development of practical solution methods for MICP remains a challenge.

In this talk, we discuss our recent developments in the design of algorithmic tools for MICP. Inspired by the pivotal role of disjunctive cutting-planes in the algorithmic advances for MILP, we develop the theory of disjunctive cutting-surfaces for mixed-integer second order cone programming (MISOCP). To this end, we consider two-term disjunctions on the second-order cone and its affine cross-sections. The resulting nonconvex sets are of fundamental importance in MISOCP where they can be used to derive valid inequalities that strengthen the problem formulation. We identify the cases where the convex hull of the disjunction can be completely characterized with a single second-order conic inequality, allowing efficient computation with second-order cone programming in a cutting-surface algorithm framework. This talk is based on joint works with Fatma Kilinc-Karzan and Gerard Cornuejols.

Bio: Sercan Yildiz is a PhD candidate in Algorithms, Combinatorics, and Optimization in the Tepper School of Business of Carnegie Mellon University. Previously, he received a master's degree in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University and master's and bachelor's degrees in Industrial Engineering from the University of Pittsburgh and Bogazici University, respectively. His research centers on operations research, with emphasis on the methodological and practical aspects of integer programming and non-convex optimization.

Burak Kocuk - Global Optimization Methods for Two Power Systems Problems

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 18, 2015, Friday 13:40
EA 409, Bilkent University
by
Burak Kocuk
Industrial and Systems Engineering, Bilkent University

AN M.S. THESIS PRESENTATION

Abstract:

Power engineering is concerned with the generation, transmission, and distribution of electricity over electric power network, which is arguably one of the largest engineering systems in the world. The size of electric utility industry exceeds billions of dollars and its utilization in a cost-effective manner while providing reliable accessibility is extremely important. Power system planning is a hierarchical decision making environment. In this talk, I will focus on two operational level optimization problems, namely the Optimal Power Flow Problem and the Optimal Transmission Switching Problem. The former is a nonlinear network problem and the latter is the network design version of the first one. Due to nonlinearity induced by alternating current power flow equations, these two optimization problems are nonconvex and require efficient global optimization methods. We make effective use of several different strategies to handle nonconvexity, including conic relaxations, envelopes, disjunctive extended formulations, cutting planes, variable bound tightening techniques and feasibility heuristics. Our approaches scale well to large power systems problems and provide provably good solutions in time compatible with the needs of the system operators.
Bio: Burak Kocuk received the B.S. and M.S. degrees in industrial engineering and the B.S. degree in mathematics from Boğaziçi University. Currently, he is pursuing the Ph.D. degree in operations research at the H. Milton Stewart School of Industrial and Systems Engineering of the Georgia Institute of Technology. His doctoral studies focus on global optimization problems in power systems, in particular, optimal power flow and transmission switching.

Firdevs Ulus - Primal and Dual Approximation Algorithms for Convex Vector Optimization Problems

DEPARTMENT OF INDUSTRIAL ENGINEERING

December 4, 2015, Friday 13:40
EA 409, Bilkent University
by
Firdevs Ulus
Department of Industrial Engineering,
Bilkent University


Abstract:

Vector optimization problems can be seen as extensions of multi- objective optimization problems where the partial order on the objective space is induced by a polyhedral ordering cone C. We provide two approximation algorithms for solving convex vector optimization problems (CVOPs). The algorithms work for any number of objectives and they solve the CVOP and its geometric dual problem simultaneously. The first algorithm is an extension of Benson’s outer approximation algorithm and the second one is a dual variant of it. Both algorithms provide an inner as well as an outer approximation of the (upper and lower) images. Only one scalar convex program has to be solved in each iteration. We allow objective and constraint functions that are not necessarily differentiable, and relate the approximations to an appropriate ε- solution concept. A current research direction involves the convergence analysis of the algorithms and the extensions to solve the unbounded CVOPs.

Bio: Firdevs Ulus is an Assistant Professor in the Department of Industrial Engineering at Bilkent University. She got her PhD degree in Operations Research and Financial Engineering at Princeton University; MS degree in Mathematics at Sabancı University; and Bachelor’s degree in Manufacturing Systems (Industrial) Engineering with minor degree in Mathematics also at Sabancı University. She is working on vector (multi-objective) optimization problems, algorithms and applications in financial mathematics and economics.

İ.Kuban Altınel - Optimal Berth Allocation, Time-variant Quay Crane Assignment and Scheduling with Crane Setups in Container Terminals

DEPARTMENT OF INDUSTRIAL ENGINEERING

November 27, 2015, Friday 13:40
EA 409, Bilkent University
by
İ.Kuban Altınel
Department of Industrial Engineering,
Boğaziçi University


Abstract:

We focus on the integrated seaside operations in container terminals, namely the integration of berth allocation, quay crane assignment and quay crane scheduling problems. First, we formulate a mixed-integer linear program whose exact solution gives optimal berthing positions and berthing times of the vessels, along with their crane schedules during their stay at the quay. Then, we propose an efficient cutting plane algorithm based on a decomposition scheme. Our approach deals with berthing positions of the vessels and their assigned number of cranes in each time period in a master problem, and seeks the corresponding optimal crane schedule by solving a subproblem. We prove that the crane scheduling subproblem is NP-complete under general cost settings, but can be solved in polynomial time for certain special cases. Our computational study shows that our new formulation and proposed solution method yield optimal solutions for realistic sized instances with up to sixty vessels.
Bio: İ. Kuban Altınel was born in İstanbul, Türkiye, on July 24, 1959. He graduated from Galatasaray high school in 1978. He received the B.Sc. degree in Industrial Engineering from İstanbul Technical University, İstanbul, in 1982, the M. Sc. degree in Operations Research from Columbia University, New York City, in 1985, the M. Sc. degree in Industrial Engineering and the Ph. D. degree in Operations Research from the University of Pittsburgh, Pittsburgh, in 1989 and 1990. He joined the Department of Industrial Engineering at Boğazici University, İstanbul, in 1991 and worked as an assistant professor of Operations Research until 1994. He was an associated professor of Operations Research in the same department until 1999. He has been a professor ever since. His research interests include linear and combinatorial optimization, mathematical programming applications in statistics, wireless sensor networks and machine learning.

Fehmi Tanrısever - Financial Hedging and Optimal Procurement Policies under Correlated Price and Demand

DEPARTMENT OF INDUSTRIAL ENGINEERING

November 20, 2015, Friday 13:40
EA 409, Bilkent University
by
Fehmi Tanrısever
Faculty of Business Administration,
Bilkent University


Abstract:

We consider a firm that procures an input commodity to produce an output commodity to sell to the end retailer. The retailer's demand for the output commodity is negatively correlated with the price of the output commodity. The firm can sell the output commodity to the retailer either through a spot contract or forward contract. Input and output commodity prices are also correlated and follow a joint stochastic price process. The firm maximizes shareholder value by jointly determining optimal procurement and hedging policies. We show that partial hedging dominates both perfect hedging and no-hedging when input price, output price, and demand are correlated. We characterize the optimal financial hedging and inventory policies as a function of the term structure of the commodity prices, the correlation between the input and output prices, and the firm's operating characteristics. In addition, our analysis illustrates that hedging is most beneficial when output price volatility is high and input price volatility is low. Our model is empirically tested on futures price data for corn and ethanol from the Chicago Mercantile Exchange between 4/1/2005 and 12/31/2011. This is a joint work with Ankur Goel.
Bio: Fehmi Tanrisever received his Bachelor of Science degree in Industrial Engineering from Bilkent University, Ankara, Turkey in 2002. After graduation, he joined the Operations Research and Industrial Engineering program at the University of Texas at Austin and received his Master of Science degree in Operations Research and Industrial Engineering in 2004. Subsequently, he entered the PhD program in Supply Chain and Operations Management at the McCombs School of Business and received his PhD in 2009. After receiving his PhD, he worked as an assistant professor of Operations Management and Finance at the Industrial Engineering and Innovation Sciences Department at Eindhoven University of Technology until 2013. Since September 2013, he is an assistant professor of Operations Management at Bilkent University, Ankara, Turkey. His publications have appeared in journals including Production and Operations Management, Annals of Operations Research, European Journal of Operational Research and Energy.

Zeynep Akşin - Humans in Queues: Modeling and Analysis of Customer Abandonments

DEPARTMENT OF INDUSTRIAL ENGINEERING

November 13, 2015, Friday 13:40
EA 409, Bilkent University
by
Zeynep Akşin
College of Administrative Sciences and Economics
Koç University


Abstract:

This talk will cover a stream of research focusing on modeling and empirical analysis of abandonment in queues. The standard paradigm assumes abandonment times are random variables with a given distribution. This approach does not provide a natural link between system features and abandonments. Yet in many service systems, the waiting entities are humans and make decisions regarding abandonment in response to system features. Explicit modeling of decision making by humans in queues enables the characterization of the link between system features and customer patience in queues. We propose a model for abandonment by strategic customers in queues, and subsequently estimate the delay sensitivity parameters of these customers empirically using abandonment data from a call center. We illustrate how this approach can be used to study the effect of various system design decisions, including delay announcements in call centers.
Bio: Zeynep Akşin is a Professor of Operations Management at Koç University. Prior to joining Koç University in 2002, she was a faculty member at INSEAD. She was a visiting scholar at the Kellogg School of Management, Northwestern University in 2008-2009. She holds a PhD and an M.S. in Operations Management from the Wharton School of the University of Pennsylvania, and a B.S. in Industrial Engineering from Middle East Technical University. Her research focuses on service operations management. Her research has been recognized with awards including a TÜBA Encouragement Award and an IBM Faculty Award. Dr. Akşin also serves as an Associate Editor for IIE Transactions.

Çağın Ararat - Measuring systemic risk: a model uncertainty approach

DEPARTMENT OF INDUSTRIAL ENGINEERING

23 October 2015, Friday 13:40
EA 409, Bilkent University
by
Çağın Ararat
Bilkent University


Abstract:

Systemic risk is concerned with the failure of an interconnected system as a consequence of the actions of its components. In the event of a financial crisis, it becomes important to measure and allocate the systemic risk of a network of financial institutions. In this talk, we will focus on a recent multivariate approach for measuring systemic risk where the state of the financial network is modeled as a random vector of individual equities/losses. Then, the systemic risk measure is defined as the set of all capital allocation vectors that make the “impact of the system to the society” acceptable. We present a dual representation theorem for systemic risk measures and provide a “model uncertainty” and “weight ambiguity” interpretation of the dual variables. As a special case, we will consider a financial system with exponential aggregation mechanism, where the distances of the financial institutions with respect to the society are measured in terms of relative entropies.

Ersin Körpeoğlu - Time-Based Crowdsourcing Contests

DEPARTMENT OF INDUSTRIAL ENGINEERING

2 October 2015, Friday 13:40
EA 409, Bilkent University
by
Ersin Körpeoğlu
School of Management, University College London


Abstract:

We study a contest wherein an organizer elicits solution(s) to a problem from a large population of agents at the earliest time. Each agent's solution time is random and it depends on agent’s effort and expertise. We call an agent whose ex-post solution output contributes to the organizer’s objective a contributor, and consider a general case in which the organizer minimizes the total solution time of any number of contributors. We establish that when agents’ solution times are less (resp., highly) uncertain, it is optimal for the organizer to screen and allow entry to only a small group (resp., a larger group) of highest-expertise agents. Furthermore, it is optimal for the organizer to compensate each agent based on the agent’s own solution time and on whether the organizer can observe agents’ efforts.

2014-2015

Hilal Dağ - Assortment Planning in Transshipment Systems

DEPARTMENT OF INDUSTRIAL ENGINEERING

A M.S. DISSERTATION SEMINAR

31 August 2015, Monday 10:00
EA 409, Bilkent University
by
Hilal Dağ
Department of Industrial Engineering
Bilkent University


Assortment planning, i.e., determining the set of products to offer to customers is a challenging task with immediate effects on profitability, market share and customer service. In this thesis, we study a multiple location assortment planning problem in a make-to-order environment. Each location has the flexibility to access others' assortments by transshipping products he/she does not keep. This allows them to offer higher variety and increase sales without increasing costs associated with assortment. Customer behavior is defined using exogenous demand model where each arriving customer to a location chooses a product with an exogenous probability among all possible options. In our multiple location setting, we assume that the customer has access to the complete assortment in all locations. If a customer's requested product is not available in that customer's assigned location but available in another location, the firm ships the product to the customer at the same price and incurs a transshipment cost. If his/her first choice product is not offered by any of the locations then he/she switches to a substitute product, which can be either satisfied from customer's assigned location, or by transshipment. Otherwise, it is lost. The problem is then to determine the assortment in each location such that the total expected profit is maximized.
We first show that the optimal assortments are nested, i.e., the assortment of a location with a smaller market share is a subset of the assortment of a location with a larger market share. We then show that while the common assortment is a popular set (i.e., some number of products with highest purchase probabilities), the individual assortments do not necessarily have this property. We also derive a sufficient condition for each assortment to be a popular set. In the final part of the thesis, we conduct an extensive numerical study to understand the effects of various parameters such as assortment cost and transshipment cost on optimal assortments and effects of allowing transshipments on resulting assortments compared to a no-transshipment system. Finally, we introduce an approximation algorithm that benefits from the structural properties obtained in this study and also test its performance with extensive numerical analyses.


This thesis is supervised by
Assoc. Prof. Dr. Alper Şen and Assist. Prof. Dr. Nagihan Çömez Dolgan


Halil İbrahim Bayrak - Auction Design and Optimal Allocation by Linear Programming

DEPARTMENT OF INDUSTRIAL ENGINEERING

A M.S. DISSERTATION SEMINAR

28 August 2015, Friday 10:30
EA 409, Bilkent University
by
Halil İbrahim Bayrak
Department of Industrial Engineering
Bilkent University


For the sale of a single object through an auction, we assume discrete type space for agents and make use of linear programming to find optimal mechanism design for a risk-neutral seller. First, we show that the celebrated incentive compatible mechanism, second price auction, is not optimal. We find a slightly different optimal mechanism referred to as “discrete second price auction”. Secondly we look at the problem of allocation with costly inspection. We obtain the optimal solution in the form of a favored-agent mechanism by the Greedy Algorithm. Lastly, we relax the common prior assumption and maximize the worst-case utility of an ambiguity averse seller for the two problems mentioned above. While the problem does not yield a useful optimal mechanism in general, optimal solutions for some special cases are obtained.


This thesis is supervised by
Prof. Mustafa Ç. Pınar


Çağıl Koçyiğit - ROBUST AUCTION DESIGN UNDER MULTIPLE PRIORS

DEPARTMENT OF INDUSTRIAL ENGINEERING

A PH.D. DISSERTATION SEMINAR

24 July 2015, Friday 14:30
EA 409, Bilkent University
by
Çağıl Koçyiğit
Department of Industrial Engineering
Bilkent University


In optimal auction design literature, it is a common assumption that valuations of buyers are independently drawn from a unique distribution. In this thesis, we study auctions with ambiguity for a relaxed environment where valuation distribution is uncertain itself and introduce a linear programming approach to robust auction design problem. We develop an algorithm that gives the optimal solution to the problem under certain assumptions when the seller is ambiguity averse with prior set P and the buyers are ambiguity neutral with a prior f ϵ P. Also, we consider the case where the buyers are ambiguity averse as the seller and formulate this problem as a mixed integer programming problem. Then, we propose a hybrid algorithm that enables to achieve a good solution for this problem in a reduced time.


This thesis is supervised by
Prof. Mustafa Ç. Pınar


Vedat Bayram - NONLINEAR MIXED INTEGER PROGRAMMING MODELS AND ALGORITHMS FOR FAIR AND EFFICIENT LARGE SCALE EVACUATION PLANNING

DEPARTMENT OF INDUSTRIAL ENGINEERING

A PH.D. DISSERTATION SEMINAR

24 July 2015, Friday 09:00
EA 409, Bilkent University
by
Vedat Bayram
Department of Industrial Engineering
Bilkent University


“Shelters are safe facilities that protect a population from possible damaging effects of a disaster. Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the trade-off between efficiency and fairness. Disasters are uncertain events. Related studies and real-life practices show that a significant uncertainty regarding the evacuation demand and the impact of the disaster on the infrastructure exists. The second model we propose is a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to shelters and paths to minimize the expected total evacuation time, under uncertainty. The model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for an impending earthquake in Istanbul, Turkey. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values. We also propose an exact algorithm based on Benders decomposition to solve the stochastic problem. To the best of our knowledge, ours is the first algorithm that uses duality results for second order cone programming in a Benders decomposition setting. We solve practical size problems with up to 1000 scenarios in moderate CPU times. We investigate methods such as employing a multi-cut strategy, deriving pareto-optimal cuts, using a reduced primal subproblem and preemptive priority multiobjective program to enhance the proposed algorithm. Computational results confirm the efficiency of our algorithm.


This thesis is supervised by
Prof. Hande Yaman


Esra Koca - LOT SIZING WITH NONLINEAR PRODUCTION COST FUNCTIONS

DEPARTMENT OF INDUSTRIAL ENGINEERING

A PH.D. DISSERTATION SEMINAR

23 July 2015, Thursday 15:00
EA 409, Bilkent University
by
Esra Koca
Department of Industrial Engineering
Bilkent University


In this study, we consider different variations of the lot sizing problem encountered in many real life production, procurement and transportation systems. First, we consider the deterministic lot sizing problem with piecewise concave production cost functions. A piecewise concave function can represent quantity discounts, subcontracting, overloading, minimum order quantities, and capacities. Computational complexity of this problem was an open question in the literature. We develop a dynamic programming (DP) algorithm to solve the problem and show that the problem is polynomially solvable when number of breakpoints of the production cost function is fixed and the breakpoints are time invariant. We observe that the time complexity of our algorithm is as good as the complexity of existing algorithms in the literature for the special cases with capacities, minimum order quantities, and subcontracting. Our algorithm performs quite well for small and medium instances. For larger instances, we propose a DP based heuristic to find a good quality solution in reasonable time.
Next, we consider the stochastic lot sizing problem with controllable processing times where processing times can be reduced in return for extra compression cost. We assume that the compression cost function is a convex function in order to reflect the increasing marginal cost of larger reductions in processing times. We formulate the problem as a second-order cone mixed integer program, strengthen the formulation and solve it by a commercial solver. Moreover, we obtain some convex hull and computational complexity results. We conduct an extensive computational study to see the effect of controllable processing times in solution quality and observe that even with small reductions in processing times, it is possible to obtain a less costly production plan.
As a final problem, we study the multistage stochastic lot sizing problem with nervousness considerations and controllable processing times. System nervousness is one of the main problems of dynamic solution strategies developed for stochastic lot sizing problems. We formulate the problem so that the nervousness of the system is restricted by some additional constraints and parameters. Mixing and continuous mixing set structures are observed as relaxations of our formulation. We develop valid inequalities for the problem based on these structures and computationally test these inequalities.


This thesis is supervised by
Prof. Hande Yaman

Prof. M Selim Aktürk


Merve Meraklı - Hub Location Problems under Polyhedral Demand Uncertainty

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

23 July 2015, Thursday 10:30
EA 409, Bilkent University
by
Merve Meraklı
Department of Industrial Engineering
Bilkent University


Hubs are points of consolidation and transshipment in many-to-many distribution systems that benefit from economies of scale. In hub location problems, the aim is to locate hub facilities such that each pairwise demand is satisfied and the total cost is minimized. The problem usually arises in the strategic planning phase prior to observing actual demand values. Hence incorporating robustness into hub location decisions under data uncertainty is crucial for achieving a reliable hub network design. In this thesis, we study hub location problems under polyhedral demand uncertainty. We consider uncapacitated multiple allocation p-hub median problem under hose and hybrid demand uncertainty and capacitated multiple allocation hub location problem under hose demand uncertainty. We propose mixed integer linear programming formulations and devise several exact solution algorithms based on Benders decomposition in order to solve large-scale problem instances. Computational experiments are performed on instances of three benchmark data sets from the literature.


This thesis is supervised by
Prof. Hande Yaman


Tuğçe Vural - Shipment Consolidation Under Different Delivery Date Options For E-tailing

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

9 June 2015, Monday 10:00
EA 202, Bilkent University
by
Tuğçe Vural
Department of Industrial Engineering
Bilkent University


In this thesis, we consider a shipment consolidation problem for an e-retailer company which has two type of services for its customers:``regular'' and ``extended''. In the regular service, the e-retailer guarantees a delivery time to its customers. However, in the extended service, customers get their items in negligible or zero amount of time, such as same-day delivery, supplied physical inventories located sufficiently close. When a shipment decision is made, it serves both customers of the regular service and small inventories for the extended service. In our study, we analyze shipment consolidation operation given these two services for both deterministic and stochastic demand structure. In the deterministic demand problem, our average profit maximizing model decides the optimal service choice and we provide optimality conditions, an algorithm to find optimal solution, numerical and structural analyses. In the stochastic demand setting, we evaluate the problem firstly for the regular service which has Poisson demand. Then, we expand the problem by including the extended service which has deterministic demand. For this problem, we present a modified policy for the shipment operation, and compare the performance of our policy with simulation to provide an insight.


This thesis is supervised by
Prof. Nesim Erkip


Barış Yıldız - DYNAMIC PRICING UNDER INVENTORY CONSIDERATIONS AND PRICE PROTECTION

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

11 May 2015, Monday 09:40
EA 202, Bilkent University
by
Barış Yıldız
Department of Industrial Engineering
Bilkent University


In high-tech industry, customers’ tendency to purchase the newest versions of products forces manufacturers to reduce the prices of older models. This puts the retailers in a vulnerable position as their own sales prices also decrease for these products. For this purpose, manufacturers and retailers compromise over different price commitment terms in their contracts. One such term is price protection. In general, a price protection term obliges a manufacturer refund a retailer a portion of the difference between the new and old wholesale prices for the inventory that the retailer have in stock and that are ordered within a time window. Sometimes, refunds may also be applied on products sold based on their sales price. We study a price protection contract over a finite horizon under stochastic demand. We have a single manufacturer and a single retailer, each endowed with a fixed amount of inventory at the beginning of the horizon. The manufacturer determines the retail price and neither manufacturing nor replenishment is allowed. The objective of the manufacturer is to set the retail price in each period given how much inventory is left at the manufacturer and the retailer. We analyze the structure of the model and provide some structural results on the effect of different factors on optimal prices and optimal expected profits. Then, we present the results of a numerical study in which we further investigate the effect of different factors to obtain managerial insights.


This thesis is supervised by
Assoc. Prof. Alper Şen


Banu Lokman - Finding nondominated points for multi-objective integer programs and its applications

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

17 April 2015, Friday 13:40
EA 409, Bilkent University
by
Banu Lokman
Department of Industrial Engineering
Middle East Technical University


Finding nondominated points is typically hard for Multi-objective Integer Programs (MOIPs) and the number of nondominated points is large when the objectives are conflicting. It is not practical for a decision maker (DM) to compare all of these points to make a decision. Therefore, it is important to generate nondominated points in a region that is of interest to the DM. In this study, we first present two exact algorithms that efficiently generate all nondominated points for MOIPs. We then develop a variation of those algorithms that generates the true nondominated points in any specified region for MOIPs. To define the preferred region of the DM, we also develop a procedure that first approximates the nondominated set using a hypersurface, finds a preferred hypothetical point on this surface and then defines a preferred region around the hypothetical point. Once the preferred region is defined, all nondominated points in that region are generated. We test the performance of the algorithms on some multi-objective combinatorial optimization problems and demonstrate that the algorithms work well.
This is a joint work with Murat Köksalan.
Short Bio:
Banu Lokman is an Assistant Professor in the Industrial Engineering (IE) Department of Middle East Technical University (METU). After receiving her BS degree in 2005, she worked for ASELSAN as a planning engineer during 2005-2006. She received her MS degree in 2007 and her PhD degree in 2011 from the IE department of METU. She was a research assistant in the same department from 2006 to 2011. She joined TED University in 2012. From May 2012 to June 2013, she worked as a visiting researcher in School of Business of Aalto University, on leave from TED University. She has been a member of the faculty in IE Department of METU since September, 2014. Her current research interests are under the umbrella of multi-criteria decision making, in areas such as combinatorial optimization, evolutionary algorithms, and applications.

Özlem Çavuş - Computational Methods for Risk-Averse Undiscounted Transient Markov Models

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

20 March 2015, Friday 13:40
EA 409, Bilkent University
by
Özlem Çavuş
Bilkent University


The total cost problem for discrete-time controlled transient Markov models is considered. The objective functional is a Markov dynamic risk measure of the total cost. Two solution methods, value and policy iteration, are proposed, and their convergence is analyzed. In the policy iteration method, we propose two algorithms for policy evaluation: the nonsmooth Newton method and convex programming, and we prove their convergence. The results are illustrated on a credit limit control problem.
This is a joint work with Dr. Andrzej Ruszczynski from Rutgers Business School.
Short Bio:
Özlem Çavuş is currently an Assistant Professor of Industrial Engineering at Bilkent University. She received her B.S. and M.S. degrees in Industrial Engineering from Boğaziçi University in 2004 and 2007, respectively, and the Ph.D. degree in Operations Research from Rutgers Center for Operations Research (RUTCOR) at Rutgers University in 2012. Her research interests include stochastic optimization, risk-averse optimization, Markov decision processes, and healthcare applications.

Orhan Karasakal - Missile and Sector Allocation for a Naval Task Group

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

6 March 2015, Friday 13:40
EA 409, Bilkent University
by
Orhan Karasakal
Turkish Naval Forces


A naval task group (TG) is a collection of naval combatants and auxiliaries that are grouped together for the accomplishment of one or more missions. In this talk, we present two interrelated problems for a naval task group.
The first problem, missile allocation problem (MAP) can be defined as the optimal allocation of a set of surface-to-air missiles (SAMs) of a naval task group to a set of attacking air targets. MAP is a new treatment of an emerging problem fostered by the rapid increase in the capabilities of anti-ship missiles (ASMs), the different levels of air defense capabilities of the warships against the ASM threat, and new technology that enables a fully coordinated and collective defense. In addition to allocating SAMs to ASMs, MAP also schedules launching of SAM rounds according to shoot-look- shoot engagement policy or its variations, considering multiple SAM systems and ASM types. MAP can be used for air defense planning under a given scenario. As thorough scenario analysis would require repetitive use of MAP, we propose efficient heuristic procedures for solving the problem.
In the second problem, we address the sector allocation for a naval TG. Ships forming a TG are located in predefined sectors. We define determination of ship sector locations to provide a robust air defense formation as the sector allocation problem (SAP). A robust formation is one that is very effective against a variety of attack scenarios but not necessarily the most effective against any scenario. We propose a 0- 1 integer linear programming formulation for SAP. The model takes the size and the direction of threat into account as well as the defensive weapons of the naval TG by utilizing MAP solutions. We develop tight lower and upper bounds by incorporating some valid inequalities and use a branch and bound algorithm to exactly solve SAP.
Short Bio:
Dr. Karasakal is associate professor and Director of Scientific Decision Support Division at the Turkish Naval Forces Command HQs. He holds a Ph.D. in Industrial Engineering from Middle East Technical University, an M.S. in Operations Research from the Naval Postgraduate School and a bachelor’s degree from the Turkish Naval Academy. He spent a year at the Operational Research Division of the Canadian Department of National Defence as a defense science fellow. Dr. Karasakal’s research focuses on military applications of operations research, with particular emphasis on air defense, base location, vehicle routing, sortie optimization, force structure and concept analysis.

Dilek Keyf - A Simulation Optimization for Breast Cancer Screening in Turkey

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

27 February 2015, Friday 13:30
EA 409, Bilkent University
by
Dilek Keyf
Bilkent University


Breast cancer is the most common cancer type among women in the world. 6.3 million women were diagnosed with breast cancer between 2007-2012 and 25% of cancers in women are breast cancer. Early diagnosis and early detection has an important role in survival from breast cancer. Mammographic screening is proved to be the only screening method that can reduce breast cancer mortality. Even though mammographic screening has this significant benefit, it is expensive and it can decrease life quality and it can generate false positive results. As a consequence, recommending an effective and cost-efficient mammographic screening policy in terms of starting and ending ages and screening frequencies has high importance. This study aims to optimize Ada’s Breast Cancer Simulation Model using Simulated Annealing. This model was run for Turkish women born in 1980 during their lifetime. The purpose of this study is to obtain an optimal or near optimal policy in terms of life years gained and cost for Turkish women. This study also aims to demonstrate the outcomes in terms of effectiveness and cost when different combinations of policy variables are used.
Keywords: Breast cancer, simulated annealing, simulation optimization, screening policy


This thesis is supervised by
Asst. Prof. Özlem Çavuş
Prof. İhsan Sabuncuoğlu

Ayşe Selin Kocaman - Macro-level Hybrid Energy Planning Model with Intermittent Renewable Sources, India Case Study

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

20 February 2015, Friday 13:40
EA 409, Bilkent University
by
Ayşe Selin Kocaman
Bilkent University


Current supply for electricity generation mostly relies on fossil fuels. International Energy Agency (IEA) estimated that primary sources of electricity in 2011 consisted of 41.3% coal, 21.9% natural gas and 4.8% petroleum summing up to a 68% share for fossil fuels in primary electricity consumption in the world. However, fossil fuels are finite and their combustion results in greenhouse gas emissions, which contribute to global warming and health hazards. Therefore, energy models that involve clean and renewable energy sources are necessitated to ease the concerns on the electricity generation that meets the projected demand.
In this talk, we explore the challenges, opportunities and ways of integrating renewable sources into the grid. We discuss the problem of modeling hydro and solar energy production, allocation and storage, capturing the stochastic nature of hourly supply and demand data. Here, we mathematically model two hybrid energy generation and allocation systems where time variability and uncertainty of energy sources and demand is balanced using the water stored in the reservoirs. In the first model, we use conventional hydro power stations (incoming stream flows are stored in large dams and water release is deferred until it is needed) and in the second model, we use pumped hydro stations (water is pumped from lower reservoir to upper reservoir during periods of low demand to be released for generation when demand is high). An innovative contribution of this work is the establishment of a new perspective to energy modeling by including fine-grained sources of uncertainty such as stream flow and solar radiations in hourly level as well as spatial location of supply and demand in national level. The model will be presented for a future scenario of India and helps answer whether solar energy, in addition to huge hydropower potential in Himalayas, would be enough to meet growing electricity demand if fossil fuels could be almost completely phased out from electricity generation.
Short Bio:
Selin Kocaman is an Assistant Professor in Industrial Engineering Department at Bilkent University. She received her B.S. degree in Industrial Engineering from Bogazici University, M.S. degree in Operations Research and Ph.D. degree in Sustainable Energy, both from Columbia University. At Columbia University, she was a member of Sustainable Engineering Lab. Her research interests include energy infrastructure design and planning, optimization of electric power systems, sustainable energy resources and sustainable development. Her work has been recognized by Production and Operations Management Society (POMS) by winning College of Sustainable Operations Ph.D. Proposal Award (2011).

Çağın Ararat - Multivariate risk: utility-based risk, risk subject to frictions, risk evolution in continuous time

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

28 January 2015, Wednesday 13:40
EA 409, Bilkent University
by
Çağın Ararat
Princeton University


Quantification of risk in multi-asset markets has recently gained interest in financial mathematics. In the existence of frictions in such markets, multiple portfolio vectors could possibly be used in order to compensate the risk of a financial position. These portfolios are said to form the set-valued risk of the position. In this talk, we will first discuss two classes of set-valued risk measures that are defined with respect to utility- based risk preferences. These risk measures can also be formulated as set optimization problems and the two classes are related via a recent Lagrange duality for such problems. Examples include the multivariate versions of the well-known entropic risk measure and average value at risk. Then, in a general convex market model, we will consider how market frictions such as transaction costs can be taken into account in the evaluation of set-valued risk measures. In the last part of the talk, we will discuss a continuous time framework where set-valued risk measures are defined via backward stochastic differential inclusions. As an analytical tool in the study of these inclusions, we will discuss a characterization theorem for the Aumann integrals (or expectations) of set-valued functions (or random sets).
Based on joint works with Birgit Rudloff and Andreas Hamel.
BIO:
Cagin Ararat received his BS degree in 2010 from the Department of Industrial Engineering at Bilkent University. He is currently a PhD candidate in the Department of Operations Research and Financial Engineering at Princeton University. His research interests include financial mathematics, markets with transaction costs, risk measurement, multivariate risk, risk preferences, random sets and set optimization. He is a member of SIAM, INFORMS and AMS.

Firdevs Ulus - Vector Optimization Problems, Algorithms and Financial Applications

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

23 January 2015, Friday 13:40
EA 409, Bilkent University
by
Firdevs Ulus
Princeton University


A parametric simplex algorithm for solving linear vector optimization problems (LVOPs) and two approximation algorithms for solving convex vector optimization problems (CVOPs) are provided. The algorithms work for any number of objectives, and it is possible to extent them to any polyhedral ordering cone C. The parametric simplex algorithm solves the LVOP as long as it has a solution. In each iteration, it provides a set of inequalities, which defines the current partition of the parameter space. In addition to the usual simplex arguments, one needs to eliminate the redundant inequalities from that set. This extra step is similar to yet simpler than the vertex enumeration procedure, which is used in most of the objective space based LVOP algorithms. Different from those, this algorithm doesn’t require to solve a scalar linear program in each iteration. The approximation algorithms solve the CVOP and its geometric dual problem simultaneously. The first algorithm is an extension of Benson’s outer approximation algorithm, and the second one is a dual variant of it. Both algorithms provide an inner as well as an outer approximation of the (upper and lower) images. Only one scalar convex program has to be solved in each iteration. We allow objective and constraint functions that are not necessarily differentiable, and relate the approximations to an appropriate ε-solution concept. Some financial applications are provided.

Vehicle Routing with Soft Time Windows and Stochastic Travel Times: A Column Generation and Branch-and-Price Solution Approach

Duygu Taş -

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

6 January 2015, Tuesday 13:40
EA 409, Bilkent University
by
Duygu Taş
HEC Montréal and CIRRELT, Montréal, Canada


We study a vehicle routing problem with soft time windows and stochastic travel times. In this problem, we consider stochastic travel times to obtain routes which are both efficient and reliable. In our problem setting, soft time windows allow early and late servicing at customers by incurring some penalty costs. The objective is to minimize the sum of transportation costs and service costs. Transportation costs result from three elements which are the total distance traveled, the number of vehicles used and the total expected overtime of the drivers. Service costs are incurred for early and late arrivals; these correspond to time-window violations at the customers. We apply a column generation procedure to solve this problem. The master problem can be modeled as a classical set partitioning problem. The pricing subproblem, for each vehicle, corresponds to an elementary shortest path problem with resource constraints. To generate an integer solution, we embed our column generation procedure within a branch- and-price method. Extensive computational results are obtained by experimenting with well- known problem instances.
Short Bio
Dr.Duygu Taş holds a B.Sc. degree in Mechanical Engineering from the Faculty of Engineering at Boğaziçi University (2006). She received her M.Sc. degree in Industrial Engineering from the Faculty of Engineering and Natural Sciences at Sabancı University (2008). She carried out her Ph.D. studies at the School of Industrial Engineering and Innovation Sciences at the Eindhoven University of Technology (2009–2013). She conducted a part of her Ph.D. research at CIRRELT - Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation. Since completing her Ph.D., she has been working as a post-doctoral fellow at HEC Montréal and CIRRELT. Dr.Taş has published her research in leading academic journals including Computers & Operations Research, European Journal of Operational Research, and Transportation Research Part C: Emerging Technologies. She frequently presents her research at academic and professional conferences, and also serves as a reviewer for numerous scientific international journals in operations research. Her research is mainly focused on production and inventory planning under uncertainty, transportation planning under uncertainty, and airline crew scheduling.

A. Serdar Şimşek - Who Should Set the Prices? Analysis of Pricing Decentralization in Customized Pricing Systems

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

29 December 2014, Monday 13:40
EA 202, Bilkent University
by
A. Serdar Şimşek
Cornell University


In many markets, it is common for headquarters to create a price list but grant local salespeople discretion to negotiate prices for individual transactions. How much (if any) pricing discretion headquarters should grant is a topic of debate within many firms. We investigate this issue using a unique data set from an indirect lender with local pricing discretion. We estimate that the local sales force adjusted prices in a way that improved profits by approximately 11% on average. A counterfactual analysis shows that using a centralized, data-driven pricing optimization system could improve profits even further, up to 20% over those actually realized. This suggests that centralized pricing –if appropriately optimized– can be more effective than field price discretion. We then propose and analyze a structural estimation method based on the expectation-maximization algorithm that allows for the customer’s willingness-to-pay and sales agent’s reserve price distributions to depend on an arbitrary set of covariates. Our estimation method generates accurate estimates of customer-price response and the prices of lost deals. The estimated distributions can also be used to optimize controls on negotiated prices that can significantly increase revenues relative to both unconstrained negotiations and centrally-optimized fixed prices.
This is joint work with Robert Phillips and Garrett van Ryzin from Columbia University.

Işıl Alev - Operational Perspectives on Extended Producer Responsibility for Durable and Consumable Products

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

26 December 2014, Friday 13:40
EA 409, Bilkent University
by
Işıl Alev
Georgia Institute of Technology


In response to growing post-consumer waste and associated environmental concerns, environmental policies have become increasingly widespread around the world. In this context, Extended Producer Responsibility (EPR) – a concept that holds producers responsible for environmentally safe treatment of their post-consumer products by imposing collection and recycling/disposal obligations – has gained traction for durable products such as electronics and consumable products such as pharmaceuticals. In this talk, we present operational perspectives on the effective implementation of EPR for these product categories.
The main part of the talk focuses on durable products: Although there is a rich body of analysis on EPR-based policies for products with durable nature, the existing approach ignores durability of the products and presence of secondary markets. Motivated by this gap in the analysis, we identify the interaction between EPR and secondary markets through analyzing the effect of EPR obligations on a durable good producer's secondary market strategy. Based on this interaction, we show that EPR may result in unintended adverse environmental outcomes such as curbing product reuse, shortening useful lives of products, and increasing production. In this context, we also identify the impact of trade regulations in the form of export restrictions to developing countries. We show that seemingly effective restrictions on export can further exacerbate the unintended effect of EPR on the production and reuse levels. These findings allow us to provide policy guidelines for effective EPR implementations in the context of durable products.
This is joint work with Atalay Atasu (College of Business, Georgia Institute of Technology), Vishal Agrawal (School of Business, Georgetown University), Özlem Ergun (Mechanical and Industrial Engineering, Northeastern University), and Beril L. Toktay (College of Business, Georgia Institute of Technology).
BIO
Işıl Alev is a Ph.D. candidate in the H. Milton Steward School of Industrial & Systems Engineering at the Georgia Institute of Technology. She is a former Johnson Fellow and current Sam Nunn Security Fellow. Her research interests lie in sustainable operations management with a particular emphasis on business practices subject to environmental responsibility. Her research in this area received the 2013 POMS Sustainable Operations Dissertation Proposal Award. She earned a B.Sc. degree in Industrial Engineering at the Middle East Technical University in Turkey in 2009 and a M.Sc. degree in Industrial Engineering at the Georgia Institute of Technology in 2012.

Ersin Körpeoglu - Innovation Tournaments with Multiple Contributors

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

24 December 2014, Wednesday 13:40
EA 409, Bilkent University
by
Ersin Körpeoglu
Tepper School of Business
Carnegie Mellon University


This paper studies an innovation tournament in which an organizer seeks solutions to an innovation-related problem from a number of independent agents. While agents exert efforts to improve their solutions, their outcomes are unknown a priori due to technical uncertainty and subjective taste of the organizer. We call an agent whose ex-post output contributes to the organizer’s utility a contributor, and consider a general case in which the organizer seeks any number of contributors. We show that a winner-takes-all award scheme is optimal to the tournament organizer for a large class (but not all) of distributions for agents’ uncertain outputs. In this case, when the spread of the output distribution or the number of contributors is sufficiently large, an open tournament that does not restrict entry of participants is optimal. Finally, we compare the organizer’s payoffs under different compensation rules that award participants based on their relative ranks, absolute performance or a combination of both.

Tuğçe Işık - Server Flexibility in Non-collaborative Systems

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

22 December 2014, Monday 13:40
EA 409, Bilkent University
by
Tuğçe Işık
Georgia Institute of Technology


Server flexibility can be used to improve various performance measures of manufacturing and service systems. One advantage of server flexibility is the possibility of collaboration since each server can perform multiple tasks. However, collaboration may not always be possible due to the physical limitations in the system or the nature of the tasks. In this talk, we focus on non-collaborative systems with flexible servers and examine how server allocation decisions should be made.
In the first part, we consider a tandem line with finite buffers and equal number of tasks and servers, where the servers can be reallocated instantaneously at no cost. We prove that a threshold-type server allocation policy is optimal for systems of two stations and develop near-optimal server allocation heuristics for larger systems. We also provide a numerical comparison of collaborative, non-collaborative and non- flexible systems, and show that server flexibility can indefinitely improve the long- run average throughput in the absence of collaboration. In the second part, we assume that each server reallocation results in a setup cost. For a two-station system with homogeneous tasks and constant setup costs, we characterize the optimal policy, and investigate how the optimal policy depends on the magnitude of the setup cost and the buffer size. For systems with non-constant setup costs and/or non-homogeneous tasks, near-optimal heuristics are provided. Finally, we consider larger queueing networks with infinite buffers, and propose a linear program that yields an upper bound on the achievable long-run average throughput. Furthermore, we introduce a class of round- robin policies that asymptotically achieve this upper bound for systems with two stations, and are near-optimal for longer lines even when the buffers are finite.

Simge Küçükyavuz - Decomposition Algorithms for Two-Stage Chance-Constrained Programs

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

19 December 2014, Friday 13:40
EA 409, Bilkent University
by
Simge Küçükyavuz
Ohio State University


We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where ``recovery" decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility cuts to solve this class of problems. Computational results on a chance-constrained resource planing problem indicate that our algorithms are highly effective in solving these problems compared to a mixed-integer programming reformulation and a naive decomposition method.
This is joint work with Xiao Liu and Jim Luedtke
Bio: Simge Küçükyavuz is an Associate Professor in the Integrated Systems Engineering Department at the Ohio State University. She received her MSc and PhD degrees from the University of California, Berkeley, and her BS degree from the Middle East Technical University. Her interests are in mixed-integer programming, large-scale optimization, optimization under uncertainty, and their applications. Her research is supported by multiple grants from the National Science Foundation, including the 2011 CAREER Award. She serves on the editorial boards of several journals, including Computational Optimization and Applications, Networks, and Journal of Global Optimization.

Nilay Noyan - Optimization with multivariate stochastic preference constraints

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

5 December 2014, Friday 13:40
EA 409, Bilkent University
by
Nilay Noyan
Mechanical Engineering
Sabancı University


For many decision making problems under uncertainty, it is crucial to specify decision makers' risk preferences based on multiple stochastic performance measures. We consider a class of multicriteria stochastic optimization problems that feature risk- averse benchmarking constraints based on conditional value-at-risk and second- order stochastic dominance. We develop alternative mixed-integer programming formulations and solution methods for cut generation problems arising in optimization under such multivariate risk constraints. We give the complete linear description of two non-convex substructures appearing in these cut generation problems. We present computational results that show the effectiveness of our proposed models and methods.
(Joint work with Simge Küçükyavuz, The Ohio State University)
Nilay Noyan is an Associate Professor in the Manufacturing Systems and Industrial Engineering Program at Sabancı University, Turkey. She is a recipient of the Young Scientist (BAGEP) Research Award of the Science Academy, Turkey. She received her Ph.D. degree in operations research from Rutgers University, USA, in 2006. Her research interests include optimization, stochastic programming, risk modeling, and stochastic optimization applications with an emphasis on sustainable urban transportation, airline revenue management, and disaster relief network design.

İlker Temizer - Optimization in Computational Interface Mechanics

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

28 November 2014, Friday 13:40
EA 409, Bilkent University
by
İlker Temizer
Mechanical Engineering
Bilkent University


Optimization can play a significant role in computational mechanics. In this talk, I will present two interface mechanics problems where the choice of the optimization algorithm determines accuracy, efficiency and robustness. The first half of the talk will be devoted to contact mechanics problems which are highly nonlinear primarily due to large deformations and the presence of contact constraints. Specifically, I will concentrate on the formulation and application of a class of interior point methods to the weak enforcement of the constraints. In the second half, I will summarize our ongoing work on micro-texture design for thin-film lubrication. Here, the problem is posed in the context of the homogenization theory which constitutes a link between the objective function and the pixelated optimization space. Overall, I aim to give a balanced overview of our goals (mechanics and computation) and the employed tools (discretization and optimization).
Bio:

İlker Temizer received his B.S. degree (2001) from Boğaziçi University and his M.S. (2003) and Ph.D. (2005) degrees from the University of California, Berkeley in Mechanical Engineering. Subsequently, he joined the Institute of Continuum Mechanics of the Leibniz University at Hannover as a post-doctoral researcher where he held teaching and research responsibilities in addition to the leadership of a junior research group. Since September 2010, he is a faculty member at Bilkent University. His research efforts are focused on the theoretical and numerical aspects of computational mechanics that are associated with multiscale-multiphysics modeling strategies for heterogeneous materials and interfaces.

Onur Boyabatlı - Capacity Management in Agricultural Commodity Processing and Application in the Palm Industry

DEPARTMENT OF INDUSTRIAL ENGINEERING

JOINT IE/MAN SEMINAR ANNOUNCEMENT

21 November 2014, Friday 13:40
EA 409, Bilkent University
by
Onur Boyabatlı
Lee Kong Chian School of Business
Singapore Management University


This paper studies the capacity investment decisions of an agri-processor that uses a commodity input to produce a commodity output and a byproduct. Using a multi-period model, we study the one-time processing and (output) storage capacity investment decisions, and the periodic processing and inventory decisions in the presence of input and output spot price uncertainties and uncertain production yield. We identify three capacity investment strategies, investing in storage-dominating, processing-dominating or mixed portfolio, and provide conditions under which each strategy is optimal. Using a calibration based on the palm industry, we provide rules of thumb for capacity management: The processor should decrease its processing capacity with an increase in price correlation; and with an increase (a decrease) in input or output price volatility when this volatility is low (high). The storage capacity should be adjusted in a similar fashion only as a response to a change in output price volatility, otherwise it should not be altered. We find that not accounting for the byproduct revenue or inventory holding possibility in capacity planning leads to sizeable profit loss. Ignoring production yield uncertainty has a significant negative impact on profitability if the capacity planning is made based on the maximum yield possible, as often done in practice; but it has an insignificant impact if the planning is made based on the average yield.
Co-authored with:
Quang (Jason) D. Nguyen, Carlson School of Management, University of Minnesota Tong Wang, National University of Singapore
Bio:

Onur Boyabatli is Assistant Professor of Operations Management at the Lee Kong Chian School of Business, Singapore Management University. He holds a Ph.D. in Technology and Operations Management from INSEAD, University, Turkey.

His research interests are in the area of integrated risk management in supply chains. In particular, he studies several operational issues (such as capacity management, procurement, farm-yield management, product pricing) of supply chains agents (such as farmers, processors) in different agricultural industries (such as live-animal, oilseeds, cocoa, grains). He also studies the technology and capacity management including integration with financing and financial risk management in capital- intensive industries (such as automotive).
His research papers have been published in Management Science. He is the co-editor of “Handbook of Integrated Risk Management in Global Supply Chains.” He was awarded the Management Science Distinguished Service Award in 2012. He was nominated for the IFORM (Interface of Finance, Operations and Risk Management) SIG (Special Interest Group) Chair in 2014. He co-organized the IFORM at the MSOM conference in 2012 and 2014, the 5th Management in Operations and Global Supply Chains in 2008, and co-chaired the M&SOM-IFORM Cluster at the 2012 and 2013 INFORMS Annual Meetings.

Emre Nadar - A Novel Optimal Policy Structure for Managing Inventory in Assemble-to-Order Systems

DEPARTMENT OF INDUSTRIAL ENGINEERING

JOINT CS/IE SEMINAR ANNOUNCEMENT

24 October 2014, Friday 13:40
EA 409, Bilkent University
by
Emre Nadar
Information Systems Technology and Design
Singapore University of Technology and Design


Assemble-to-order (ATO) systems are popular in many industries including, but not limited to, automotive, consumer electronics, and online retailing, where offering customized products and services within a short response window plays a vital role. Although popular in practice, characterizing optimal control policies for ATO systems has remained a longstanding challenge in the field of Operations Management. This talk will present our progress towards solving this important and difficult problem.

In the first part, we address the problem of inventory replenishment and allocation for a manufacturer who sells an assembled product as well as individual spare parts. We model the problem as a Markov decision process, with state space consisting of component inventory levels. By partitioning the state space into multiple disjoint lattices based on products’ component requirements, we establish the optimality of base-stock production and stock rationing policies on each lattice. Our computational results reveal the practicality of such lattice-dependent base-stock and rationing policies for ATO systems with general product structures.

In the second part, we present an approximate dynamic programming method to the general ATO problem, under Markovian assumptions on production and demand. We approximate the optimal cost function by reducing the state space of the original problem via a novel aggregation technique, which may significantly alleviate the computational burden. We show the optimality of lattice-dependent base-stock and rationing policies for the aggregate problem. We also derive error bounds for this approximation and provide computational results. We conclude with a discussion of future research directions in this field.

Parts of this research are joint work with Alp Akcay from Bilkent University, Mustafa Akan and Alan Scheller-Wolf from the Tepper School of Business, Carnegie Mellon University.

Short Bio: Emre Nadar is an assistant professor in the Department of Industrial Engineering at Bilkent University. He received his B.S. degree in Industrial Engineering from Bilkent University, and his M.S. and Ph.D. degrees in Operations Management from the Tepper School of Business, Carnegie Mellon University. His research interests include stochastic dynamic programming, queueing theory, supply chain management, and sustainable operations.

David Yau - Cyber-Physical Security in Future Cities

DEPARTMENT OF INDUSTRIAL ENGINEERING

JOINT CS/IE SEMINAR ANNOUNCEMENT

17 October 2014, Friday 13:40
EA 409, Bilkent University
by
David Yau
Information Systems Technology and Design
Singapore University of Technology and Design


Digital control and communication are being used to modernize urban infrastructures, such as electrical grids and water systems, to meet the increasing demands of future cities for size, performance, and sustainability. While the added intelligence provides desirable performance features, it also adds to the system's complexity, which raises equally critical concerns for safety and security. In this talk, I will overview research that analyzes cyber-physical system (CPS) vulnerabilities such as real-time consumer pricing in emerging smart-grid demand response. I will also propose a design methodology to monitor the (not fully trustworthy) high performance operation of a smart grid, but assure its fallback to a system drifts too close to unsafety.

Bio:

David Yau received the B.Sc. (first class honors) from the Chinese University of Hong Kong, and M.S. and Ph.D. from the University of Texas at Austin, all in computer science. He has been Professor of Information Systems Technology and Design at SUTD since 2013. Since 2010, he has been Distinguished Scientist at the Advanced Digital Sciences Center, Singapore, where he leads the Smart Grid IT research program. Prior to Singapore, he was Associate Professor of Computer Science at Purdue University (West Lafayette), USA.

David’s research interests are in network protocol design and implementation, CPS security and privacy, quality of service, network incentives, and wireless and sensor networks. He received a CAREER award from the U.S. National Science Foundation. He was also the recipient of an IBM PhD Fellowship. He won Best Paper award from the 2010 IEEE International Conference on Multi-sensor Fusion and Integration (MFI). His recent papers in 2008 IEEE Int'l Conf. Mobile Ad-hoc and Sensor Systems (MASS), 2013 IEEE Int'l Conf. Pervasive Computing and Communications (PerCom), 2013 IEEE Int'l Conf. Cyber-Physical Systems, Networks, and Applications (CPSNA), and 2013 ACM BuildSys were Best Paper finalists.

Dündar Kocaoğlu - Technology and Innovation Management

DEPARTMENT OF INDUSTRIAL ENGINEERING

JOINT IE/MAN SEMINAR ANNOUNCEMENT

10 October 2014, Friday 13:40
EA 409, Bilkent University
by
Dündar Kocaoğlu
Engineering and Technology Management Department
Portland State University


A historical perspective on societal changes is given; critical issues, challenges and myths of technology management are presented; management of change in technical organizations is explained; and technological leadership is discussed in this presentation.

BIO:
Dundar F. Kocaoglu is Founding Chairman and Professor of the Engineering and Technology Management Department at Portland State University, Director of RISE (Research Institute for Sustainable Energy), and President and CEO of PICMET (Portland International Center for Management of Engineering and Technology). His research areas include decision theory, technology management, project management, innovation management, technology evaluation, R&D management, and resource optimization.

Dr. Kocaoglu received his B.S. in Civil Engineering from Robert College in 1960, M.S. in Structural Engineering from Lehigh University in 1962, M.S. in Industrial Engineering from the University of Pittsburgh in 1972, and Ph.D. in Operations Research and Systems Management, also from the University of Pittsburgh in 1976. He joined Portland State University to start the Engineering Management Program in 1987. The program has become the Engineering and Technology Management Department. It now has more than 250 graduate students, including about 40 pursuing their PhD degrees. Prior to 1987, Dr. Kocaoglu was the director of a similar program at the University of Pittsburgh. He has supervised 26 PhD dissertations in the two universities.

Dr. Kocaoglu has worked in industry as an engineer and project manager from 1962 to 1971. He has been a consultant on engineering and technology management since 1973. His clients include Westinghouse, Brown Boveri, IBM, Intel Corporation, Hewlett-Packard, Xerox, Tektronix, II- Morrow, Cascade Microtech, several other small-to-medium sized technology-based companies, more than 20 universities, R&D Centers and the United Nations. He has served in National Research Council committees for the evaluation of the NIST (National Institute of Science and Technology) manufacturing centers, and for the improvement of U.S. Department of Energy decision making processes for nuclear waste disposal and decommissioning. He has also been an NSF (National Science Foundation) reviewer for research proposals, and served on NSF panels for the evaluation of research centers.

Dr. Kocaoglu has published about a hundred papers, delivered keynote speeches and made more than 200 presentations on engineering and technology management in international conferences. He is the author, editor or co-editor of six books. He was the Editor-in-chief of *IEEE Transactions on Engineering Management* from 1986 to 2003, and the Series Editor of John Wiley Book Series in Engineering & Technology Management from 1985 through 1998. Dr. Kocaoglu is the recipient of the IEEE Fellow Award, IEEE Centennial Medal, IEEE Millennium Medal and PICMET Fellow Award, all of which were awarded for "leadership in the development of the Engineering Management discipline". He was also named Distinguished Research Mentor by the National Science Council of Taiwan. Dr. Kocaoglu is listed in *Who Is Who In The World, Who Is Who In America, Who Is Who In Education, Who Is Who in Engineering, American Men and Women in Science* and more than a dozen other reference publications.

2013-2014

Nihal Berktaş - Solution Methodologies for Debris Removal During Response Phase

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

24 July 2014, Thursday 17:30
EA 409, Bilkent University
by
Nihal Berktaş
Department of Industrial Engineering
Bilkent University


During the disaster response phase of the emergency relief, the aim is to reduce loss of human life by reaching disaster affected areas with relief items as soon as possible. Debris caused by the disaster blocks the roads and prevents emergency aid teams to access the disaster affected regions. Deciding which roads to clean in order to transport relief items is crucial to diminish the negative impact of a disaster on human health. Despite the significance of the problem during response, in the literature debris removal is mostly studied in recovery or reconstruction phases of a disaster. The aim of this study is providing solution methodologies for debris removal problem in response phase. In particular, debris removal activities on certain blocked arcs have to be scheduled in order to reach a set of critical nodes such as schools and hospitals. Two mathematical models are developed with different objectives. The first model aims to minimize the total time spent to reach all critical nodes whereas the second minimizes weighted sum of visiting times where weights indicate the priorities of critical nodes. Since obtaining solutions quickly is important in the early post- disaster, heuristic algorithms are also proposed. Two data sets belonging to Kartal and Bakırkoy districts of İstanbul are used to test the mathematical models and heuristics.
Keywords: Debris management, debris removal, relief transportation, node routing.


This thesis is supervised by
Assoc. Prof. Dr. Bahar Yetiş
Assoc. Prof. Dr. Oya Karaşan

Malek Ebadi - BUYBACK AND TARGET REBATE CONTRACTS WITH A CARBON TRADER MANUFACTURER

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

23 July 2014, Wednesday 13:40
EA 202, Bilkent University
by
Malek Ebadi
Department of Industrial Engineering
Bilkent University


In this study, the coordination of a manufacturer and a retailer in a supply chain is considered, in a single period environment where the manufacturer has carbon emission restriction with trade option. The customer demand over a period is assumed to be a random variable with an arbitrary distribution. Two types of contracts, namely the buyback and the target rebate contracts are considered. For each type, the contract parameters which achieve channel coordination have been studied. The models show that for both contract types, under specific parameter settings coordination is achievable. In particular, we show that under a buyback contract with a carbon trader manufacturer, coordination can be achieved, even if no returns are allowed, contrary to the findings of Pasternack, who first studied a buyback contract in a setting where carbon emissions are not taken into consideration. The results are illustrated by numerical examples.


This thesis is supervised by
Prof. Dr. Ülkü Gürler

Kumru Ada - A SIMULATION MODEL FOR BREAST CANCER EPIDEMIOLOGY IN TURKEY

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

21 July 2014, Monday 10:00
EA 409, Bilkent University
by
Kumru Ada
Department of Industrial Engineering
Bilkent University


Breast cancer has a vital importance in women's life. In the world, breast cancer incidence and mortality rates are increasing. Considering the burden of disease, every year 1.4 million women get breast cancer and about 500,000 women die due to breast cancer. Breast cancer has a high incidence and mortality rates especially in developing countries, where late diagnosis of cancer is also increasing the disease burden. Lack of knowledge of the exact causes of breast cancer increases the importance of early detection. The most effective way of early detection is to apply mammography screening. Screening the accurate target population increases the rate of early detection of breast cancer and lessens the economic and health burden of disease. In this study, two simulation models were constructed in order to analyze the population-based mammography screening programs for Turkey. One model was run for 10 years for validation purpose while the other one was run for the women born in 1980 during their lifetime to analyze several screening programs. Costs and health outcomes of the screening policies were examined by doing cost-effectiveness analysis. By doing this analysis with second model, a good screening policy is determined in terms of beginning and final age of screening and screening frequency.
Keywords: Breast cancer, screening programs, mammography, simulation


This thesis is supervised by
Asst. Prof. Özlem Çavuş
Prof. Dr. İhsan Sabuncuoğlu

Nil Karacaoğlu - An Application of Stochastic Programming on Robust Airline Scheduling

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

21 July 2014, Monday 14:00
EA-409, Bilkent University
by
Nil Karacaoğlu
Department of Industrial Engineering
Bilkent University


The aim of this study is to create flight schedules which are less susceptible to unexpected flight delays. To this end, we examine the block time of the flight in two parts, cruise time and non-cruise time. The cruise time is accepted as controllable within some limit and it is taken as a decision variable in our model. The non-cruise time is open to variations. In order to consider the variability of non-cruise times in the planning stage, we propose a nonlinear mixed integer two stage stochastic programming model which takes the non-cruise time scenarios as input. The published departure times of flights are determined in the first stage and the actual schedule is decided on the second stage depending on the non-cruise times. The objective is to minimize the airline's operating and passenger dissatisfaction cost. Fuel and CO2 emission costs are nonlinear and this nonlinearity is handled by second order conic inequalities. Two heuristics are proposed to solve the problem when the size of networks and the number of scenarios increase. A computational study is conducted using the data of a major U.S. carrier. We compare the solutions of our stochastic model with the ones found by using expected values of non-cruise times and the company's published schedule.
Keywords: Airline Scheduling, Stochastic Programming, Robust Optimization, Nonlinear Programming


This thesis is supervised by
Prof. Selim Aktürk
Prof. Hande Yaman

Özüm Korkmaz - Carbon Restricted Newsvendor Problem Under CVAR Objective and Resource Constraints

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

17 July 2014 - Thursday 13:40
EA-409, Bilkent University
by
Özüm Korkmaz
Department of Industrial Engineering
Bilkent University


Newsboy problem has been studied in the literature extensively. The classical newsvendor, representing the risk neutral decision maker, determines the optimal order/production quantity by maximizing the expected profit or minimizing the expected total cost of a single period with stochastic demand. This approach is not suitable if one also aims to reduce the chances of facing unexpected losses due to demand uncertainty. In this thesis, two problems are investigated with a single product newsvendor under CVaR maximization objective. The first problem addresses the newsvendor model with two different carbon emission reduction policies, namely, mandatory emission allowance and carbon emission trading mechanism. In the second problem, as an extension of the first one, a newsvendor with multiple resource constraints is considered for the cases where the resources have quotas with trade options. Analytical expressions for optimal order/production quantities are determined together with the optimal trading policy and numerical examples are provided.
Keywords: Newsvendor, CVaR, Carbon Emissions, Cap and Trade.


This thesis is supervised by
Prof. Ülkü Gürler

Oğuz Çetin - EXACT SOLUTIONS AND HEURISTICS FOR MULTI-PRODUCT INVENTORY PRICING PROBLEM

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

9 July, 2014 - Wednesday 14:00
EA-409, Bilkent University
by
Oğuz Çetin
Department of Industrial Engineering
Bilkent University


We study the multi-product inventory pricing problem under stochastic and price sensitive demand. We have initial inventory of m resources whose different combinations form n products. Products are perishable and need to be sold by a deadline. Demand for each product is modeled as a non- homogeneous Poisson process whose intensity is a function of the current price only. The aim is to set the price of each product over the selling period to maximize the expected revenue. This problem is faced in various industries including retail, airlines, automobile, apparel, hotels and car rentals. Our contributions are twofold. First, we provide a closed form solution for the special case of exponential price response where the elasticity parameters of the demand function of all products are equal. Second, we develop two classes of dynamic pricing heuristics: one using the value approximation approach of dynamic programming and the other using the deterministic version of the problem. Our numerical analysis indicates that dynamic pricing yields significantly higher revenues compared to fixed price policies. One of the dynamic pricing heuristics based on the deterministic problem provides around 5-15% additional revenue compared to fixed price policies. Moreover, the two value approximation heuristics that we suggest result in at most 0.5% and 3.4% gaps in the expected revenue compared to the optimal dynamic pricing policy for general form of exponential price response. These additional revenues can have a profound effect on the profitability of firms, so dynamic pricing should be preferred over fixed price policies in practice.
Keywords: Dynamic pricing, network yield management, inventory pricing


This thesis is supervised by
Assoc. Prof. Alper Şen

OPTIMAL EXERCISE OF COLLAR TYPE AND MULTIPLE TYPE PERPETUAL AMERICAN STOCK OPTIONS IN DISCRETE TIME WITH Emre Kara - LINEAR PROGRAMMING

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

26 June, 2014 - Thursday 10:00
EA-409, Bilkent University
by
Emre Kara
Department of Industrial Engineering
Bilkent University


An American option is an option that entitles the holder to buy or sell an asset at a pre-determined price at any time within the period of the option contract. A perpetual American option does not have an expiration date. In this study, we solve the optimal stopping problem of a perpetual American stock option from optimization point of view using linear programming duality under the assumption that underlying’s price follows a discrete time and discrete state Markov process. We formulate the problem with an infinite dimensional linear program and obtain an optimal stopping strategy showing the set of stock- prices for which the option should be exercised. We show that the optimal strategy is to exercise the option when the stock price hits a special critical value. We consider the problem under the following stock price movement scenario: We use a Markov chain model with absorption at zero, where at each step the stock price moves up by ∆x with probability p, and moves down by ∆x with probability q and does not change with probability 1 − (p + q). We examine two special type of exotic options. In the first case, we propose a closed form formula when the option is collar type. In the second case we study multiple type options, that are written on multiple assets, and characterize the exercise region for different multiple type options.


This thesis is supervised by
Prof. Mustafa Ç. Pınar

AN INTEGRATED APPROACH FOR ROBUST AIRLINE SCHEDULING, Hüseyin Gürkan - AIRCRAFT FLEETING AND ROUTING WITH CRUISE SPEED CONTROL

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

1 July, 2014 - Thursday 10:30
EA-409, Bilkent University
by
Hüseyin Gürkan
Department of Industrial Engineering
Bilkent University


To place emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate robust schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers' connection service levels are ensured via chance constraints and maintenance requirements are satisfied. We propose a nonlinear mixed integer programming model. In the objective function, the cost functions due to fuel consumption and CO2 emission cost involve nonlinearity. This nonlinearity is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of efficient aircraft and even to decrease total number of aircraft needed while satisfying same service level and maintenance requirements for aircraft fleeting and routing. Besides, for the robust schedule design problem, it is possible to improve the solution since a routing decision could eliminate the necessity of inserting idle time or compressing cruise time. In addition, we propose two heuristic methods to solve large size problems faster than the integrated model. Eventually, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.


This thesis is supervised by
Prof. Selim Aktürk
Assoc. Prof. Sinan Gürel

Ozan Candoğan - Optimal Multiperiod Pricing with Service Guarantees

DEPARTMENT OF INDUSTRIAL ENGINEERING

SEMINAR ANNOUNCEMENT

11 June 2014 Wednesday 13:40 - Thursday 10:00
EA-409, Bilkent University
by
Ozan Candoğan
Duke University


We study the multiperiod pricing problem of a service firm with capacity levels that vary over time. Customers are heterogeneous in their arrival and departure periods as well as valuations, and are fully strategic with respect to their purchasing decisions. The firm’s problem is to set a sequence of prices that maximizes its revenue while guaranteeing service to all paying customers. We provide a dynamic programming based algorithm that computes the optimal sequence of prices for this problem in polynomial time. We show that due to the presence of strategic customers, available service capacity at a time period may bind the price offered at another time period. This phenomenon leads the firm to utilize the same price in multiple periods, in effect limiting the number of different prices that the service firm utilizes in optimal price policies. Also, when customers become more strategic (patient for service), the firm offers higher prices. This may lead to the underutilization of capacity, lower revenues, and reduced customer welfare. We observe that the firm can combat this problem ability, beyond posted prices, to direct customers to different service periods.
This is a joint work with Christian Borgs (Microsoft), Jennifer Chayes (Microsoft), Ilan Lobel (NYU), and Hamid Nazerzadeh (USC).
*Bio: *Ozan Candogan is an assistant professor at the Fuqua School of Business, where he is a member of the Decision Sciences area. Prior to joining Fuqua, he received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. His research interests are in mechanism design, decision making in social and economic networks, and analysis of dynamic strategic interactions. He is a recipient of the 2012 Microsoft Research Ph.D. fellowship, and 2009 Siebel Scholarship.

Bengisu Sert - MODELING AND OPTIMIZATION OF MICRO SCALE POCKET MILLING OPERATIONS

DEPARTMENT OF INDUSTRIAL ENGINEERING

AN M.S. THESIS PRESENTATION

22 May, 2014 - Thursday 10:00
EA-202, Bilkent University
by
Bengisu Sert
Department of Industrial Engineering
Bilkent University


Manufacturing of micro scale parts and components made from engineering materials having complex three dimensional surfaces are used in today’s high value added products. These components are commonly used in biomedical and consumer electronics industries and fabrication of micro parts at a low cost without sacrificing quality is a challenge. Micro mechanical milling is a viable technique which can be used to produce micro parts, however the existing knowledge base on micro milling is limited compared to macro scale machining operations.
The subject of this thesis is micro scale pocket milling operations used in micro mold making which are used in micro plastic injection in mass production polymer micro parts. Modeling of pocket milling while machining of basic pocket shapes are considered first. The developed milling model is then extended to more complex mold shapes. Minimum total production time is used as the objective to solve single pass, multi pass, and multi tool problems. Case studies are presented for each problem type considering the practical issues in micro milling. A software has been developed to optimize machining parameters and it is shown that the developed pocket milling optimization model can successfully be used in process planning studies.
Keywords: Micro milling, tool path generation, sharp corner milling, pocket milling, optimization


This thesis is supervised by
Asst Prof. Yiğit Karpat

Ayşegül Toptal - Environmental Inventory Models

DEPARTMENT OF INDUSTRIAL ENGINEERING

25 April, 2014 - at:13:40
EA-409, Bilkent University
by
Ayşegül Toptal
Department of Industrial Engineering
Bilkent University


As the world economy becomes increasingly conscious of the environmental concerns, evidence suggests that companies who make better business decisions to consider the interests of other stakeholders, including the human and natural environments, will succeed. While the environmental regulation policies aim to protect consumers, employees and the environment, cost of compliance should not deter companies to do business. To meet regulatory requirements, firms can change their production/inventory related operations and/or invest in green technologies. In this talk, we introduce two inventory models for providing guidance to the companies to make better inventory decisions while utilizing the available environmental technologies, and for helping policy-makers understand the implications of the different regulation policies on the profitability of a company.
In the first model that will be introduced, we analyze a retailer’s joint decisions on inventory replenishment and emission reduction investment under three emission regulation policies; cap, tax and cap-and-trade. Our results provide guidelines and insights about five issues: (i) how much the retailer should order at each replenishment and how much he/she should invest in emission reduction to minimize long-run average costs, (ii) what the impact of having an investment option is on the retailer’s annual costs and emissions, (iii) how the retailer’s annual costs and emissions under an emission regulation policy compare to those when no regulation is in place, (iv) how the retailer should choose among different investment options available and (v) how the different regulation policies compare in terms of the retailer’s annual emissions and costs. In the second model, we also consider the retailer’s supplier, and study coordination and contracting between the two parties. We propose coordination mechanisms for this system to increase its profitability. We show that, even though such coordination mechanisms help the retailer and the supplier decrease their costs without violating the emission regulations, they may result in increased carbon emission under certain circumstances.

Alp Akçay - Quantifying Input Uncertainty in Simulation Output: Dependent Input Models of Diverse Types

DEPARTMENT OF INDUSTRIAL ENGINEERING

18 April, 2014 - at:13:40
EA-409, Bilkent University
by
Alp Akçay
Department of Industrial Engineering
Bilkent University


When we use simulation to estimate the performance of a stochastic system, the simulation model often includes multivariate input models (e.g., product demands and exchange rates in a global supply chain, processing times of a workpiece across several work centers) that are estimated using finite samples of real-world data. The finiteness of the samples introduces estimation errors in the input models, affecting the simulation output. However, the propogation of input model uncertainty to output uncertainty is rarely considered in simulation output analysis.
In this seminar, a discussion of input uncertainty issues and the state-of-the-art approaches for input uncertainty quantification -- that evolve around independent input models -- will be presented. There are very recent studies in the literature to account for input uncertainty with correlated input variables. However, their common input modeling technique assumes continuous marginal distributions for each input. In this study, we will use an alternative modeling methodology called normal copula graphical model that allows us to learn about the dependence between input variables which can be binary, categorical with ordered categories, count or continuous. In particular, we first encode the conditional indepedencies between input variables in an undirected graph. We then build a Markov chain Monte Carlo algorithm to obtain the posterior distribution of the precision matrix associated with a set of multivariate-normal latent variables that underlie the original multivariate input model. We use this posterior distribution to account for the input uncertainty due to the unknown dependence structure. Subsequently, we capture the input uncertainty due to the unknown marginal distribution of each input variable, and present a metamodel-assisted framework to propagate the input uncertainty to the output uncertainty. Finally, we consider a continuous- review assemble-to-order system with a correlated demand-arrival process, and show that our model improves the coverage of the confidence intervals for the per-period expected cost. The seminar will conclude with an overlook of the future research directions in this area.

Yiğit Karpat - Macro/Micro Scale Machining Modeling of Difficult-to-Cut Materials

DEPARTMENT OF INDUSTRIAL ENGINEERING

11 April, 2014 - at:13:40
EA-409, Bilkent University
by
Yiğit Karpat
Department of Industrial Engineering
Bilkent University


The subject of this talk is macro- and micro-scale machining modeling of some difficult-to-cut materials. Our group’s research focus is the modeling of material and manufacturing process interactions, and we aim to develop physics-based machining models and use them to improve the manufacturability of such materials. This talk will present our research on macro-scale machining of carbon fiber reinforced polymers, which have become an important material in the aerospace industry and micro- scale machining of titanium alloys, which are commonly used in biomedical parts. In addition, some future research directions will be discussed.
Short Bio:
Yiğit Karpat received his B.Sc from Dokuz Eylül University, and his M.Sc from Middle East Technical University, both in Mechanical Engineering. In 2007, he received his Ph.D in Industrial/Manufacturing Engineering from Rutgers University and joined Bilkent University as an Assistant Professor. He currently holds dual appointments in the Department of Industrial Engineering and the Department of Mechanical Engineering at Bilkent, where he pursues research on modeling material/process interactions in manufacturing and is actively involved in the Micro System Design and Manufacturing Center established at Bilkent University with support from the Ministry of Development.

Oya Karaşan - Regenerators as Hubs

DEPARTMENT OF INDUSTRIAL ENGINEERING

4 April, 2014 - at:13:40
EA-409, Bilkent University
by
Oya Karaşan
Department of Industrial Engineering
Bilkent University


In a telecommunications network the reach of an optical signal is the maximum distance it can traverse before its quality degrades. Regenerators are devices to extend the optical reach. The regenerator placement problem seeks to place the minimum number of regenerators in an optical network so as to facilitate the communication of a signal between any node pair.
In this talk, the regenerator location problem is revisited from the hub location perspective and two new facets of it involving the challenges of survivability are introduced. Under partial survivability, our designs hedge against failures in the regeneration equipment only whereas under full survivability failures on any of the network nodes are accounted for by the utilization of extra regeneration equipment. All three variations of the problem are discussed in a unifying framework involving the introduction of individual flow-based compact formulations as well as cut formulations and the implementation of branch and cut algorithms based on the cut formulations. We report computational results evaluating the performances of the proposed solution methodologies and propose possible future research extensions in flexible optical networks.
(Joint work with B. Yıldız)

Sakine Batun - SCHEDULING MULTIPLE OPERATING ROOMS UNDER UNCERTAINTY

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 28 March, 2014 - at:13:40
EA-409, Bilkent University
by
Sakine Batun
Department of Industrial Engineering
Middle East Technical University


Surgery represents a large portion of a typical hospital’s total expenses and revenues. Therefore, operating room (OR) scheduling is an important operational problem for health care delivery systems. These problems are computationally difficult to solve due to not only their combinatorial structure and the existence of multiple resources such as ORs and surgeons, but also the uncertainty in surgery durations.
In this study, we first consider a stochastic multi-OR scheduling problem with multiple surgeons where the daily scheduling decisions are made before the resolution of uncertainty. We formulate the problem as a two-stage stochastic mixed-integer program that minimizes the sum of the fixed cost of opening ORs and the expected overtime and surgeon idling cost. We describe some structural properties of our model and derive a set of widely applicable valid inequalities that make it possible to solve realistic-sized instances. We utilize stage-wise decomposition methods to solve our model, and we use our numerical results to quantify the value of capturing uncertainty and the benefit of pooling ORs, and to demonstrate the impact of parallel surgery processing on surgery schedules.
We then consider a stochastic multi-OR scheduling problem where the initial schedule is revised under perfect information at a prespecified rescheduling point during the surgical day. We use stage-wise and scenario-wise decomposition methods to solve our rescheduling model. By using our results, we estimate the value of rescheduling, and illustrate the impact of different surgery sequencing rules on this value.
This research is joint work with Brian Denton (Department of Industrial and Operations Engineering, University of Michigan), Todd Huschka (Department of Health Care Policy and Research, Mayo Clinic), and Andrew Schaefer (Department of Industrial Engineering, University of Pittsburgh).
Bio: Sakine Batun is an assistant professor in the Department of Industrial Engineering at METU. She received her B.S. and M.S. degrees in Industrial Engineering from METU, and her Ph.D. degree in Industrial Engineering from the University of Pittsburgh. Prior to joining the faculty at METU, she was a postdoctoral fellow at the Richard Ivey School of Business, University of Western Ontario. Her primary research interests are in decision making under uncertainty, with applications in healthcare delivery, medical decision making, production and maintenance scheduling.

Kağan Gökbayrak - Developing a Gateway Deployment Plan for Wireless Mesh Networks

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 21 March, 2014 - at:13:40
EA-409, Bilkent University
by
Kağan Gökbayrak
Department of Industrial Engineering


Wireless mesh networks (WMNs) are communication networks that provide last mile Internet access. They consist of radio nodes organized in a mesh topology that communicate via wireless media in a multihop fashion. A few of these nodes (we call them gateways) have wired connections to the Internet, and provide access for the whole network. Wireless interference caused by simultaneous transmissions over the same frequency channel complicates the design problems of WMNs. In order to avoid wireless interference, link capacities are divided in time or frequency into transmission slots, and these should be allocated to non-interfering transmissions. In this presentation, we consider the problem of designing and operating a WMN, whose node demands increase at the same rate over time. Our goal is to determine a gateway deployment plan that meets the demand at all times and minimizes the total present value of the investment costs. We present an integer programming formulation that jointly determines the locations of the gateway nodes, their deployment times, the routing trees rooted at these gateways, and the transmission slot assignments to wireless links. We determine tight upper and lower bounds on the optimal cost of this NP- hard problem. We introduce valid inequalities and heuristic solution methods, and provide numerical examples to evaluate their performances.

Kagan Gokbayrak received the B.S. degrees in mathematics and in electrical engineering from Bogazici University, Istanbul, in 1995, the M.S. degree in electrical and computer engineering from the University of Massachusetts, Amherst, in 1997, and the Ph.D. degree in manufacturing engineering from Boston University, Boston, MA, in 2001. From 2001 to 2003, he worked as a network planning engineer at Genuity, Inc., Burlington, MA. In addition to preparing demand forecasts and IP-layer capacity plans of the AS1 backbone, Dr. Gokbayrak played a key role in adapting emerging IP technologies. Since 2003, he has been a faculty member in the Industrial Engineering Department, Bilkent University, Ankara, Turkey. During his sabbatical leave in 2013, he was a visiting scholar at Massachusetts Institute of Technology and an adjunct professor at Boston University. His research interests include the optimization of discrete-event and hybrid systems, wireless mesh networks, and sensor networks, and computer simulation with applications to inventory, healthcare and manufacturing systems.

Kemal Göler - Split-award Procurement Auctions: Theory and Eperiments

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

Friday 7 March, 2014 - at:13:40
EA-409, Bilkent University
by
Kemal Göler
Department of Industrial Engineering


We characterize the Bayesian Nash equilibrium under two sealed-bid split-award auction formats for awarding procurement contracts in an independent private values environment with risk-neutral bidders.
These auction formats are straightforward multi-object extensions of the first-price sealed-bid auction. We find that, although the two auction mechanisms yield the same expected costs to the buyer, other aspects of the two models, including the equilibrium bidding strategies, differ in important ways. We also analyze the predictive accuracy of equilibrium strategies in the lab. Overall, the experiments suggest that risk-neutral Bayes-Nash equilibrium strategies serve as a surprisingly accurate model for human bidding behavior in split-award auctions and that strategic complexity is an unlikely explanation for deviations from the equilibrium strategy.

Short Bio:
Kemal Goler is a researcher at the Deparment of Industrial Engineering at Bilkent. Prior to Bilkent, he worked as a principal research scientist at Hewlett- Packard Laboratories, Palo Alto,California, as an assistant professor of Economics at the University of Houston, Houston, Texas, and as visiting professor at Rice University, Houston, Texas and at Cleveland State University, Cleveland, Ohio. His research interests are in applied mechanism design - game theory, econometrics, and behavioral economics - with applications in economics and OR/OM. He holds a B.A. degree from Bogazici, an M.Sc. degree from Baylor University, both in Economics, and a Ph.D. degree in Social Science from Caltech.

Özlem Karsu - Inequity-averse decisions in operational research

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

Friday 28 February, 2014 - at:10:30
EA-409, Bilkent University
by
Özlem Karsu
London School of Economics and Political Science


There are various real life problems where equity concerns naturally arise and it is important to address these concerns for the proposed solutions to be acceptable and implementable. In this seminar, we discuss methods to incorporate two equity-related concerns into the decision making process and seek equitable solutions to different operational research problems.
In the first part, we discuss a multi-criteria decision making approach to handle distributional equity concerns. We consider situations where we have person anonymity and quasi- concavity (convexity) conditions on the model of the decision maker’s (DM) preferences. For such situations, we provide an interactive solution process which takes the DM’s value judgments into account. We introduce substantial new theory and design an interactive ranking algorithm based on our theoretical findings.
In the second part, we discuss another equity-related concept: balance concerns in resource allocation decisions. These arise when the DM wants to achieve a desired balance in the allocation over different categories of entities. Unlike distributional equity concerns, balance concerns do not involve the anonymity assumption. We provide a bi-criteria framework to think about trading balance off against efficiency in resource allocation problems and propose optimization and heuristic algorithms for our bi-criteria allocation models. We illustrate our approach by applying it to a portfolio selection problem faced by a funding agency and to randomly generated large-sized problem instances to demonstrate computational feasibility.

Parts of this research are joint work with Prof. Alec Morton from the University of Strathclyde and Nikos Argyris from the University of Warwick.

Short Bio:
Özlem Karsu is a PhD candidate at the Operational Research (Management Science) Group at the London School of Economics. She received her B.S and M.S. degrees from the Industrial Engineering Department of the Middle East Technical University, where she worked as a teaching assistant between 2008 and 2010. She also received an MPhil degree in Operational Research from the London School of Economics in 2011. Her research interests include inequity-averse decisions in operational research, interactive multi-criteria decision making approaches and applications of assignment problems.

REGINA BURACHIK - THE EXACT PENALTY MAP FOR NONSMOOTH AND NONCONVEX OPTIMIZATION

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

Friday 21 February, 2014 - at:10:30
EA-409, Bilkent University
by
REGINA BURACHIK
School of Information Technology and Mathematical Sciences
University of South Australia
Australia

Augmented Lagrangian duality provides zero duality gap and saddle point properties for nonconvex optimization. On the basis of this duality, subgradient-like methods can be applied to the (convex) dual of the original problem. These methods usually recover the optimal value of the problem, but may fail to provide a primal solution. We prove that the recovery of a primal solution by such methods can be characterized in terms of (i) the differentiability properties of the dual function, and (ii) the exact penalty properties of the primal-dual pair. We also connect the property of finite termination with exact penalty properties of the dual pair. In order to establish these facts, we associate the primal-dual pair to a penalty map. This map, which we introduce here, is a convex and globally Lipschitz function, and its epigraph encapsulates information on both primal and dual solution sets.

Short Bio:
Regina Burachik is an Associate Professor at University of South Australia, and her current research interests are nonsmooth, convex, and nonconvex optimization. She has also extensive work in the development of algorithms for nonconvex minimization. Her PhD thesis introduced and analyzed solution methods for variational inequalities, the latter being a generalization of the convex constrained optimization problem. She recently co-authored the Springer book "Set-Valued Analysis and Monotone Mappings". Regina is an associate editor of several journals from the area of optimization, including SIAM Journal on Optimization, Applied Mathematics and Computations, Communications in Mathematical Analysis, TOP, and the Australian and New Zealand Journal for Industrial Mathematics. Regina enjoys learning, teaching, supervising and collaborating with others. Her main and only reason for doing math is pleasure.

Bilgesu Çetinkaya - The Impact of Supply Chain Coordination on the Environment

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

Tuesday 21 January, 2014 - at:10:30
EA-302, Bilkent University
by
Bilgesu Çetinkaya
Department of Industrial Engineering
Bilkent University


Environmental responsibility has become an important ingredient to do business. The increased awareness of customers about the environment and the governmental regulations are pushing supply chain entities to minimize any negative influence of their operations on the environment. Therefore, companies in today’s world have to take joint responsibility with their suppliers for the environmental impact of their actions. In this thesis, we study coordination and contracting in a two-echelon supply chain (i.e., a buyer and a vendor) under three emission regulations; cap, tax and cap- and-trade. We consider a setting with deterministic demand for a single product in the infinite horizon. We model and solve the replenishment problems of the buyer and the vendor assuming decentralized and centralized decision making approaches. Based on analytical and numerical comparisons of the optimal decisions under the two approaches, we conclude that coordination may not always be good for the environment. We also propose some novel contractual agreements that involve carbon credit sharing as a term, besides the traditional mechanisms such as quantity discounts. We finally provide an extension and a preliminary analysis for the case of stochastic demand.


This thesis is supervised by
Asst Prof. Ayşegül Toptal

Duygu Tutal - MAXIMIZING PROFIT PER UNIT TIME IN COINTEGRATION BASED PAIRS TRADING

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 17 January, 2014 - at:10:00
EA-409, Bilkent University
by
Duygu Tutal
Department of Industrial Engineering
Bilkent University


Pairs Trading is a modest but persistent statistical arbitrage strategy. It is based on identifying a pair of stocks whose prices are driven by the same economic forces, and trade according to the spread between their prices. In this thesis, we study on a pairs trading method which maximizes the profit per unit time. We identify the pairs using cointegration analysis and build the method using Markov Chains. After constructing the method, we examine its performance on both simulated and real data. We use banking stocks from Istanbul Stock Exchange as real data.
Keywords: Wire Electro Discharge Grinding, Micro Tool Fabrication, Experimental Modeling, Process Modeling.


This thesis is supervised by
Assoc. Prof. Savaş Dayanık

Ali Can Ergür - MODELING OF MICRO TOOL FABRICATION PROCESS USING WIRE ELECTRO DISCHARGE GRINDING

DEPARTMENT OF INDUSTRIAL ENGINEERING

Monday 30 December, 2013 - at:10:30
EA-409, Bilkent University
by
Ali Can Ergür
Department of Industrial Engineering
Bilkent University


Fabrication of micro tools made from tungsten carbide and polysrystalline diamond is a difficult and time consuming process. Quality of the tool directly affects the dimensional integrity of the fabricated micro products. In this thesis, fabrication of micro end mills using wire electro discharge grinding (WEDG) process, a variation of electro discharge machining process, is considered. The advantage of this process is that very small micro tools (less than 0.1 mm diameter) can be produced by eroding the tool material through electrical discharges. It is preffered over traditonal grinding process since no forces are transmitted to the tool body during fabrication. However, it takes a very long time to fabricate micro tools with this method. Therefore, it is important to understand the influence of process parameters on material erosion rate in order to be able to model the process. In this study, the relationship between process input parameters and process outputs (material erosion rate and surface roughness) are investigated using experiments. A parametric formulation which allows the estimation of tool fabrication time as a function of WEDG process parameters and given tool geometry has been developed. The developed model can be used in tool geometry design optimization studies.
Keywords: Wire Electro Discharge Grinding, Micro Tool Fabrication, Experimental Modeling, Process Modeling.


This thesis is supervised by
Asst. Prof. Yiğit Karpat

Bahar Çavdar - New Approaches to Classical OR Problems (Time-Sensitive Applications)

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 27 December, 2013 - at:13:40
EA-409, Bilkent University
by
Bahar Çavdar
Georgia Institute of Technology

Desautels Faculty of Management
McGill University


In this talk, we present our work in modifying classical models to address modern computational trade-offs and behavioral observations. Most of this talk focuses on time-related issues: instead of following the conventional compute-first-implement- later approach, we develop methods to embed computation into implementation in time-sensitive applications of well-known models such as TSP and VRP. We demonstrate the effectiveness of our approach and the break-even analysis for its use. We also present our tangential work on a tour length estimation model for random graphs that compares favorably with the seminal result by Beardwood et al. (1959). Finally, we discuss the motivation for our research in updating classical models based on the results of recent behavioral studies.
This is joint work with Prof. Joel Sokol.
BIO Bahar Çavdar received her B.S. degree in Industrial Engineering with a minor in Computer Engineering from Middle East Technical University in 2009. She is currently a Ph.D. candidate at Georgia Institute of Technology. Her current research interests are in balancing computation and implementation time when solving real- time problems in time-sensitive applications and integrating human behavior into standard models.


by
Bahar Çavdar

Melih Celik - Effects of Uncertainty and Learning in Humanitarian Supply Chains: An Application in Post-Disaster Debris Clearance

DEPARTMENT OF INDUSTRIAL ENGINEERING

Wednesday 25 December, 2013 - at:13:40
EA-409, Bilkent University
by
Melih Çelik
Georgia Institute of Technology.

Desautels Faculty of Management
McGill University


One of the devastating consequences of natural and man-made disasters is the resulting debris, which can be catastrophic for the economy, environment, and the health of affected communities. Despite its importance, there is little existing work on quantitative decision support for debris management. In this talk, we discuss the post-disaster debris clearance problem, in which the objective is to establish connectivity of supply and demand for relief commodities in a timely manner by determining a sequence for the clearance of debris-blocked roads under limited resource capacity.
In many real-life applications, accurate estimation of debris amounts is not possible due to lack of information or estimation methods. As clearance proceeds, the estimations are updated based on actual observations of the debris amounts, resulting in a trade-off between learning the network structure and greedily aiming to satisfy the demand. To solve this problem optimally, we develop a partially observable Markov decision process model. Due to intensive computational requirements of this approach, we also propose novel heuristics that enhance the efficiency of the search tree. Through computational experiments, we compare the proposed approaches to those that ignore the uncertainty in the debris amounts as well as those that mimic current practice. This research is joint work with Özlem Ergun and Pınar Keskinocak from Georgia Institute of Technology. Biographical Sketch
Melih Çelik is a Ph.D. candidate in the H. Milton Stewart School of Industrial and Systems Engineering and works at the Center for Health and Humanitarian Logistics at Georgia Institute of Technology. He received his B.S. and M.S. degrees in Industrial Engineering from Middle East Technical University in 2007 and 2009, respectively. He also holds an M.S. degree in Operations Research from Georgia Institute of Technology, which he received in 2012. His research interests include stochastic and online network design, humanitarian applications of operations research, warehouse logistics, and facility location and layout.


by
Melih Celik

Mustafa Ç. PINAR - Mechanism Design: An Introduction from an Optimization Perspective

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 20 December, 2013 - at:13:40
EA-409, Bilkent University
by

Mustafa Ç. PINAR


Mechanism design is a branch of economics and game theory where a principal wishes to design a mechanism or a game that will elicit privately held information by participants and achieve at the same time the goals of the principal. The Nobel Prize in Economics 2007 was awarded to Hurwicz, Maskin and Myerson for their work in mechanism design.
The connections of the field to optimization are ubiquitous, and Myerson's seminal paper was published in Math. of OR. In this talk I intend to make a very elementary introduction to mechanism design following the recently published book ``Mechanism design a linear programming approach'' by R. Vohra, 2011, Cambridge Univ. Press. Key concepts will be introduced using an example from the theory of screening, the simplest sub-field of mechanism design where e.g., an agent sells an item to a buyer. While the item is of no value to the seller, it represents a value to the buyer, which is, however, unknown to the seller.
The seller wishes to design a mechanism that will motivate any potential buyer to reveal his valuation truthfully and at the same time maximize earnings from the sale. Under some suitable assumptions, the problem of mechanism design in this particular context is posed as a linear optimization problem. By connecting the problem to the theory of shortest paths and duality of linear programming, the optimal mechanism is derived.
Extensions and research problems are discussed.


by
Mustafa Ç. PINAR

Disjunctive Cuts for Convex MINLP: Extended Formulations

Mustafa Rasim Kılınç -

DEPARTMENT OF INDUSTRIAL ENGINEERING
A Ph.D. Dissertation Seminar

Wednesday 18 December, 2013 - at:13:40
EA-409, Bilkent University
by

Mustafa Rasim Kılınç
Carnegie Mellon University


The focus of this work is a computationally effective method for generating disjunctive cuts for convex MINLPs. The method relies on solving a sequence of cut- generating linear programs (CGLP). If the constraint functions have a special separability structure, then the polyhedral outerapproximation of the feasible region can be significantly improved by introducing auxiliary variables that separately approximate the elements of the constraint functions. These improved approximations can have significant positive benefits for linearization-based algorithms as well as the performance of the disjunctive cut generation method. The introduction of auxiliary variables can also improve the strength of the generated cuts. For instance, we have recently shown that, while the conic MIR proposed by Atamturk and Narayanan only uses an LP approximation to generate cuts for conic integer programming, the use of an extended formulation can allow the conic MIR to outperform cuts that use all the nonlinear relaxation. Our computational experiments compare the strength of this and other extended formulations for cutting plane generation and explore the strength of extensions of the conic MIR that use additional nonlinear information. This talk is based on joint work with S. Moderasi, J. Linderoth, J. Luedtke and J.P. Vielma.

Hatice Çalık - EXACT SOLUTION METHODOLOGIES FOR THE P-CENTER PROBLEM UNDER SINGLE AND MULTIPLE ALLOCATION STRATEGIES

DEPARTMENT OF INDUSTRIAL ENGINEERING
A Ph.D. Dissertation Seminar

Tuesday 17 December, 2013 - at:15:30
EA-409, Bilkent University
by

Hatice Çalık


The p-center problem is a relatively well known facility location problem that involves locating p identical facilities on a network to minimize the maximum distance between demand nodes and their closest facilities. The focus of the problem is on the minimization of the worst case service time. This sort of objective is more meaningful than total cost objectives for problems with a time sensitive service structure. A majority of applications arises in emergency service locations such as determining optimal locations of ambulances, fire stations and police stations where the human life is at stake. There is also an increased interest in p-center location and related location covering problems in the contexts of terror fighting, natural disasters and human-caused disasters. The p-center problem is NP-hard even if the network is planar with unit vertex weights, unit edge lengths and with the maximum vertex degree of 3. If the locations of the facilities are restricted to the vertices of the network, the problem is called the vertex restricted p-center problem; if the faiclities can be placed anywhere on the network, it is called the absolute p-center problem. The p-center problem with capacity restrictions on the facilities is referred to as the capacitated p- center problem and in this problem, the demand nodes can be assigned to facilities with single or multiple allocation strategies. In this thesis, the capacitated $p$-center problem under the multiple allocation strategy is studied for the first time in the literature.
The main focus of this thesis is a modelling and algorithmic perspective in the exact solution of absolute, vertex restricted and capacitated p-center problems. The existing literature is enhanced by the development of mathematical formulations that can solve typical dimensions through the use of off the-shelf commercial solvers. By using the structural properties of the proposed formulations, exact algorithms are developed. In order to increase the efficiency of the proposed formulations and algorithms in solving higher dimensional problems, new lower and upper bounds are provided and these bounds are utilized during the experimental studies. The dimensions of problems solved in this thesis are the highest reported in the literature.


This thesis is supervised by
Assoc. Prof. Oya Karaşan
Assoc. Prof. Bahar Yetiş

Beste Kucukyazici - Patient Admission and Bed Allocation Policies in Acute Care Wards; An Application to a Neurology Ward

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 13 December, 2013 - at:13:40
EA-409, Bilkent University
by

Beste Kucukyazici
Assistant Professor
Desautels Faculty of Management
McGill University


We study patient admission policies in acute care wards recognizing multiple patient types with different medical characteristics. Recent studies have shown that patients who need acute care are more effectively treated in specialized inpatient settings. The benefits of such specialized care, however, might be offset by long boarding times at the emergency department due to bed unavailability at the ward. This research is inspired by the managerial challenges at the neurology ward of Montreal Neurological Hospital (MNH), where the optimal care pathway for patients with neurological diseases is particularly time-sensitive.

We model the patient admission and bed allocation problem using an average cost dynamic programming framework. By solving the dynamic program, we determine the dynamic admission policy that provides the best care for all patients in light of limited bed availability. We present an integrated solution approach, which combines queuing methods and approximate dynamic programming. We compare the performance of the proposed policy with that of other heuristic admission policies. Our approach also has some implications for similar general multi-class queuing problems. This is joint work with Saied Samiedaluie and Vedat Verter of McGill University, and Dan Zhang of the University of Colorado at Boulder.


by
Beste Kucukyazici

Ülkü Gürler - Coordinated Logistics: Inventory Replenishment and Transportation in a Supply Chain

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 6 December, 2013 - at:13:40
EA-409, Bilkent University
by

Ülkü Gürler
Department of Industrial Engineering

In this study, we examine the coordinated replenishment and transportation functions of a supply chain with N retailers and a warehouse in the presence of stochastic demand characterized by a Poisson process. We consider two alternative transportation practices with (i) in-house fleet with capacitated trucks and (ii) contracts with a 3PL provider. We also consider two alternative replenishment policies, namely the (Q,S) and (R,T) joint replenishment policies among the retailer. For the models mentioned above we derive the operating characteristics and the expected cost rates using the stochastic properties of the underlying processes. The objective is to minimized the expected cost rates with respect to policy parameters. Pros and cons of alternative replenishment policies and transportation options are discussed via numerical examples.


by
Ülkü Gürler

Savaş Dayanık - Optimal stopping of linear diffusions

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 29 November, 2013 - at:13:40
EA-409, Bilkent University
by

Savaş Dayanık
Department of Industrial Engineering

Finding optimal times to sell a house, to retire, or to hire or fire employees requires solving optimal stopping problems. In this talk, we will show that optimal stopping problems of linear diffusions can be directly solved by means of the generalized concave functions and illustrate the solution method on several numerical examples.


by
Savaş Dayanık

Bahar Y. Kara - Humanitarian Logistics

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 22 November, 2013 - at:13:40
EA-409, Bilkent University
by

Bahar Y. Kara
Department of Industrial Engineering

According to World Food Programme (WFP) and UNICEF, “Humanitarian logistics is the process of planning, implementing, controlling the efficient, cost effective flow and storage of goods and materials, as well as related information, from point of origin to point of consumption for the purpose of meeting the end beneficiary’s requirements.” In addition to man-made and natural disasters, there are also long-term issues due to lack of basic necessities, including shelter, water, food, and access to healthcare where humanitarian actions are required. Given the complexity of the problems faced, there is a tremendous need and potential for academic research in this area. In this tutorial we will first provide an overview of some of the current research areas in humanitarian logistics and continue with some research examples from different categories of the humanitarian logistics applications including mobile blood collection, shelter area location, and debris management.


by
Bahar Y. Kara

Özgen Karaer - Improving Supplier Environmental Performance

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 15 November, 2013 - at:13:40
EA-409, Bilkent University
by

Özgen Karaer
METU
Department of Industrial Engineering

We examine how a firm can induce higher environmental quality from a supplier. A high environmental quality of the product stimulates the final demand whereas it requires a lump-sum investment and results in a unit cost increase for the supplier. Under such a setting, we examine three dynamics that the firm can leverage to entice the supplier to invest: revenue sharing, cost sharing, and supplier competition. We find that supplier competition is the most effective across the three and cost sharing produces higher investment levels when the firm is willing to take on bigger shares of the investment cost. Both may surpass the centralized solution investment level under the right circumstances. Our research is based on our work with an NGO as it markets to industries a tool to safely share chemical information.

Bio: Özgen Karaer is an assistant professor in the Department of Industrial Engineering at METU. She received her B.S. degree (2002) from Department of Industrial Engineering at METU, and her M.S. and Ph.D. (2008) degrees from Department of Management Science and Engineering at Stanford University. Prior to joining METU, she worked as a senior consultant and consequently a manager in the Operating Strategy department at Gap Inc. headquarters in San Francisco for about four years. During her tenure at Gap Inc., she worked on various projects involving replenishment prioritization, promotion hindsighting, demand planning, store inventory allocation, merchandise-test store selection, store performance measurement, and RFID. Her research is in strategic interactions in supply chains and retail management. (
This research is joint work with Tim Kraft (University of Virginia) and John Khawam (Google Inc).

Özgen Karaer
METU

Sinan Gezici - Optimal Signal and Detector Randomization under Average Power Constraints

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 8 November, 2013 - at:13:40
EA-409, Bilkent University
by

Sinan Gezici
Department of Electrical and Electronics Engineering

Optimization of signal structures and detectors is studied for communications systems under power constraints in the presence of additive noise. First, it is observed that the optimal signal for each symbol can be characterized by a discrete random variable with at most two point masses. Then, optimization over all possible signal distributions and corresponding maximum a posteriori probability (MAP) decision rules are considered. It is shown that the optimization problem can be simplified into an optimization over a number of signal parameters instead of functions, which can be solved via global optimization techniques. Improvements that can be obtained via the joint design of the signaling and the detector are illustrated via examples. Finally, related problems such as optimal detector randomization and channel swithing are briefly discussed.
(Joint work with Hui Lin)

Sinan Gezici

2012-2013

Andrew Schaefer - Optimizing the Societal Benefits of the Annual Influenza Vaccine: A Stochastic Programming ApproachOptimizing the Societal Benefits of the Annual Influenza Vaccine: A Stochastic Programming Approach

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 30 November, 2012 - at:13:40
EA-409, Bilkent University
by

Andrew Schaefer
University of Pittsburgh

Influenza (flu) is a highly contagious, acute, respiratory viral disease. Seasonal epidemics annually impact 5-15% of the world's population, resulting in 3-5 million cases of severe illness and up to 500,000 deaths. Seasonal flu epidemics caused by antigenic drifts and the high rate of influenza transmission require annual updates in the flu shot composition. The World Health Organization recommends which strains of influenza to include in each year’s vaccine based on surveillance data and epidemiological analysis.
There are two critical decisions regarding the flu shot. The first is its composition; currently three strains comprise the flu shot, and this decision affects its effectiveness against the upcoming flu epidemic. The second decision is the timing of the composition decision. If the strains are selected too early, there is a greater likelihood that there will be a mismatch. However, delaying the decision may leave a short time for manufacturing, resulting in delays or shortages in the flu shot supply.
Optimization models have not yet made a large impact on public health. Thus far, the design and timing of the flu shot have been made in an ad hoc manner.=A0 We propose a multi-stage stochastic mixed-integer program to address the optimal design of the annual flu shot design and calibrate our model with real-life data. Risk evaluation is taken into account by incorporating risk measures in the objective function. Multi-stage stochastic mixed-integer programs are very difficult optimization problems, but have many potential applications. The techniques we have developed are likely to lead to algorithmic enhancements that can be broadly applied.
This is joint work with Osman Ozaltin of the University of Waterloo, and Oleg Prokopyev and Mark Roberts of the University of Pittsburgh.

Andrew Schaefer,University of Pittsburgh

M. Selim Akturk - Airline Disruption Management with Controllable Cruise Times

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 23 November, 2012 - at:13:40
EA-409, Bilkent University
by

M. Selim Akturk
Department of Industrial Engineering
Bilkent University

Although the airline industry is very successful in implementing optimization tools for the planning and scheduling of their resources, they are facing significant problems to deal with the disruptions in their daily operations due to unexpected aircraft maintenance requirements or undesirable weather conditions. In this presentation, I first propose a novel rescheduling optimization model that explicitly accounts for delay as well as fuel burn and CO2 emission costs by incorporating cruise speed control into airline disruption management. Flight time controllability and nonlinear delay, fuel burn and CO2 emission cost functions complicate the aircraft recovery problem significantly. We utilize the recent advances in conic mixed integer programming and propose a strengthened formulation in mitigating this difficulty so that our proposed nonlinear optimization model could be used by operations controllers to deal with disruptions in real time in an optimal manner instead of relying on heuristic approaches.
On the other hand, robust airline schedules could be viewed as to generate flight schedules that are likely to minimize passenger delay. Airlines usually add an additional time, e.g. schedule padding, to scheduled gate-to-gate flight times to make their schedules less susceptible to the variability and disruptions. There is a critical tradeoff between any kind of buffer time and daily aircraft productivity. The uncertainty in flight times are modeled through chance constraints on passenger connection service levels, which are expressed using second order conic inequalities. Aircraft speed control is a practical alternative to inserting idle times into schedules. Overall, 60% saving of idle time costs could be obtained by a 2% increase in fuel costs. Our computational study shows that exact solutions can be obtained by commercial solvers such as CPLEX in seconds for a single hub schedule and in minutes for a 4-hub daily schedule of a major U.S. carrier.
This is a joint work with Prof. Alper Atamturk from UC Berkeley, Prof. Sinan Gurel from METU, and A. Serasu Duran from Northwestern University.

Biographical Sketch

M. Selim Akturk is Professor of Industrial Engineering at Bilkent University, Turkey. During his sabbatical leaves, he worked as a visiting professor at McGill University, Carnegie Mellon University and University of California at Berkeley. His current research interests include production management systems, aviation applications, and scheduling. The first part of this presentation received the `Best Technical Paper` award in 51st Annual Symposium of Airline Group of the International Federation of Operational Research Societies (AGIFORS) in 2011.

Professor Dr. M. Selim Akturk, Industrial Engineering, Bilkent University

Emre Nadar - Optimal Portfolio Strategies for New Product Development

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 16 November, 2012 - at:13:40
EA-409, Bilkent University
by

Emre Nadar
Department of Industrial Engineering
Bilkent University

We study the problem of project selection and resource allocation in a multi-stage new product development (NPD) process with stage-dependent resource constraints. We model the problem asan infinite-horizon Markov Decision Process, specifically under the discounted cost criterion. Each NPD project undergoes a different experiment at each stage of the NPD process; these experiments generate signals about the true nature of the project. Experimentation times are independent and exponentially distributed. After each experiment, beliefs about the outcome of each project are updated according to a Bayesian rule. A project thus becomes differentiated through its signals, and its category is determined by all its available signals. The state of the system is described by the number of projects in each category. A control policy specifies, given the system state, how to utilize the resources at each stage, i.e., the projects (i) to experiment with at each stage, and (ii)to terminate.
We characterize the optimal control policy according to a new strategy, state-dependent non-congestive promotion (SDNCP), for two different special cases of the general problem: (a) when there are multiple uninformative experiments; or (b) when there is a single informative experiment and projects are not terminated. An SDNCP policy implies that, at each stage, it is optimal to advance the project with the highest expected reward to the next stage if and only if the number of projects in each successor category is less than a state-dependent threshold. We further find that threshold values decrease in a non-strict sense as a later stage becomes more congested or as an earlier stage becomes less congested. These analytical findings uncover the role congestion plays in optimal policies. Our computational results support the outstanding performance of SDNCP as a heuristic for the general NPD problem.

Biographical Sketch

Emre Nadar is currently an Assistant Professor of Industrial Engineering at Bilkent University. He received his Ph.D. in Operations Management from the Tepper School of Business at Carnegie Mellon University in 2012. His research interests include Markov decision processes, queuing theory, supply chain management, and ustainable operations. He received several awards, including the first place award from the POMS Supply Chain College student paper competition and the Gerald L. Thompson doctoral dissertation award from Carnegie Mellon University. He was a finalist in both the INFORMS George Nicholson student paper competition and the MSOM student paper competition.

Assistant Professor Emre Nadar, Industrial Engineering, Bilkent University

Cem Bozansahin - The Alan Turing Way of Computing

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 9 November, 2012 - at:13:40
EA-409, Bilkent University
by

Cem Bozansahin
Professor, Graduate School of Informatics, METU

Computation is everywhere nowadays. It has changed not only our daily lives but our academic practice as well. It seems hard to believe that all these changes arose from Alan Turing's simple conception of functions unheard of before: step by step execution of an algorithm over some kind of representation. Turing seems to have begun his scientific games as a child with quite a personal way of doing things, which is very much reflected later in his life in his handling of apparently eclectic set of problems. Some connections between the problems will be highlighted in the seminar.



Biographical Sketch
Cem Bozsahin is professor of cognitive science at the Informatics Institute, METU. He holds a PhD in computer science from Arizona State University, and received his BS and MS from Industrial Engineering at METU. His research topics are nature of constraints on cognition and investigation of natural grammars.

Professor Cem Bozansahin, Graduate School of Informatics, METU

Ozlem Cavus - Risk-Averse Control of Undiscounted Transient Markov Models

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 2 November, 2012 - at:13:40
EA-409, Bilkent University
by

Ozlem Cavus
Assistant Professor of Industrial Engineering

We use Markov risk measures to formulate a risk-averse version of the undiscounted total cost problem for a transient controlled Markov process. We derive risk-averse dynamic programming equations and we show that a randomized policy may be strictly better than deterministic policies, when risk measures are employed. We propose two solution methods, value and policy iteration, and analyze their convergence. We illustrate the results on an optimal stopping problem, an organ transplant problem, and a credit limit control problem.



Biographical Sketch
Ozlem Cavus is currently an Assistant Professor of Industrial Engineering at Bilkent University. She received her B.S. and M.S. degrees in Industrial Engineering from Bogazici University in 2004 and 2007, respectively, and the Ph.D. degree in Operations Research from Rutgers Center for Operations Research (RUTCOR) at Rutgers University in 2012. Her research interests include risk-averse dynamic optimization, Markov decision processes, dynamic programming, and stochastic optimization under dominance constraints.

This is a joint work with Dr. Andrzej Ruszczynski from Rutgers Business School.

Oguzhan Alagoz - A POMDP Approach to Personalize Mammography Screening Decisions

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 5 October, 2012 - at:13:40
EA-409, Bilkent University
by

Oguzhan Alagoz
Visiting Associate Professor of Industrial Engineering

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-death in US women. Although mammography is the most effective modality for breast cancer diagnosis, it has several potential risks, including high false positive rates, which are not very rare. Therefore, the balance of benefits and risks, which dependon personal characteristics, is critical in designing a mammography screeningschedule. In contrast to prior research and existing guidelines which consider population-based screening recommendations, we propose a personalized mammography screening policy based on the prior screening history and personal riskcharacteristics of women.


We formulate a finite-horizon partially observable Markov decision process (POMDP) model for this problem. Our POMDP model incorporates two methods of detection (self or screen), age-specific unobservable disease progression, and age-specific mammography test characteristics. We use a validated micro-simulation model based on real data in estimating the parameters and solve this POMDP model optimally for individual patients. Our results show that our proposed personalized screening schedules outperform the existing guidelines with respect to the total expected quality-adjusted life years, while significantly decreasing the number of mammograms. We further find that the mammography screening threshold risk increases with age. We derive several structural properties of the model, including the sufficiency conditions that ensure the existence of a control-limit policy.


Biographical Sketch
Oguzhan Alagoz is currently a visiting associate professor at Bilkent University and associate professor of Industrial and Systems Engineering at the University of Wisconsin-Madison. In addition, he is an associate professor at the Department of Population Health Sciences and serves as the director of National Institute of Health (NIH)-funded Institute for Clinical and Translational Research-Simulation Center at UW-Madison School of Medicine and Public Health. His research interests include stochastic optimization, medical decision making, completely and partially observable Markov decision processes, simulation, risk-prediction modeling, health technology assessment, and scheduling. He is on the editorial boards of Operations Research, IIE Transactions, Medical Decision Making, and IIETransactions on Healthcare Engineering. He has received various awards including a CAREER award from National Science Foundation (NSF), Dantzig Dissertation Honorable Mention Award from INFORMS, 2nd place award from INFORMS Junior Faculty Interest Group best paper competition, best paper award from INFORMS Service Science Section, best poster award from UW Carbone Comprehensive Cancer Center. He has been the principal investigator and co-investigator on grants more than $3 million funded by NSF and the National Cancer Institute of NIH. He is a member of INFORMS, IIE, SMDM, and CISNET.

Visiting Associate Professor of Industrial Engineering
University of Wisconsin-Madison
Associate Professor of Industrial and Systems Engineering

Sibel Sözüer - On the Newsvendor Problem with Multiple Inputs under a Carbon Emission Constraint

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

7 September 2012, Friday at 10:00,
Room EA202, Engineering Building, Bilkent University
On the Newsvendor Problem with Multiple Inputs under a Carbon Emission Constraint
by

Sibel Sözüer
Department of Industrial Engineering
Bilkent University

In this thesis we consider two problems in the newsvendor setting with multiple inputs, under a carbon emission constraint and non linear production functions. In the first problem, we assume a strict carbon cap and find the optimal production quantity and input allocation that will maximize the expected profit under this constraint. In the second problem, we consider an emission trading scheme where an advance purchase of carbon emission quota is made at an initial price before the random demand is realized. When the demand is realized and new carbon trade prices are revealed , it is possible to buy additional permits or to sell an excess amount. The aim is to decide on the optimal allocation of the inputs as well as the carbon trading policy so as to maximize the expected profit. In both problems, the production quantity is linked to multiple inputs via the Cobb-Douglas and Leontieff production functions. Optimal policy structure is derived and numerical examples are provided.

Keywords: Production, Newsvendor Problem, Inventory Management, Production Function, Multiple Inputs, Carbon Emission, Carbon Cap, Emission Trading, Cobb-Douglas, Leontieff.

This thesis is supervised by
Prof. Ülkü Gürler
Assoc. Prof. Emre Berk

2011-2012

Nurcan Bozkaya - INTEGRATED MACHINE-SCHEDULING AND INVENTORY PLANNING OF DOOR MANUFACTURING OPERATIONS AT BURSA OYAK RENAULT

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

31 July 2012, Tuesday at 10:00
Room EA409, Engineering Building, Bilkent University
INTEGRATED MACHINE-SCHEDULING AND INVENTORY PLANNING OF DOOR MANUFACTURING OPERATIONS AT BURSA OYAK RENAULT
by

Nurcan Bozkaya
Department of Industrial Engineering
Bilkent University

A car passes through press, body-shell, painting and assembly stages during its manufacturing process. Due to the increased competition among car manufacturers, they aim to continuously advance and improve their processes. In this study, we analyze planning operations for the production of front/back and left/right doors in body-shell department of Bursa Oyak-Renault factory and propose a decision support system to improve their planning processes. In this study, we propose developed four different mathematical models and two heuristics approaches which decrease the current costs of the company particularly with respect to inventory carrying and setup perspectives.In the body shell department of the company, there are two parallel manufacturing cells which produces doors to be assembled on the consumption line. The effective planning and scheduling of the jobs on these lines requires solving the problem of integrated machine-scheduling and inventory planning subject to inclusive eligibility constraints and sequence independent setup times with job availability in flexible manufacturing cells of the body shell department. The novelty in the models lie in the integration of inventory planning and production scheduling decisions with the aim of streamlining operations of the door manufacturing cells with the consumption line. One of the proposed heuristic approaches is Rolling Horizon Algorithm (RHA) which divides the planning horizon into sub-intervals and solves the problem by rolling the solutions for sub-intervals. The other proposed algorithm is Two-Pass Algorithm which divides the planning horizon into sub-intervals but unlike RHA, it updates job list by adding new jobs or deleting existing ones.These approaches are implemented with Gurobi optimization software and Java programming language and applied within a decision support system that supports daily planning activities.

This thesis is supervised by
Asst. Prof. Alper Şen
Assoc. Prof. Osman Alp
Assoc. Prof. Mehmet Taner

İsmail Erikci - ANALYZING THE EFFECT OF CONSUMER RETURNS IN A MULTI-PERIOD INVENTORY SYSTEM

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

31 July 2012, Tuesday at 17:00
Room AZ 60 New York On Air, Video Conference Room, Bilkent University
ANALYZING THE EFFECT OF CONSUMER RETURNS IN A MULTI-PERIOD INVENTORY SYSTEM
by

İsmail Erikci
Department of Industrial Engineering
Bilkent University

Return of a sold item by a customer becomes tremendously common situation in many industries. Increase in the amount of returned items promotes return information to be a critical factor for inventory control. Undoubtedly another critical parameter for an inventory system is the length of the review period. Effect of the review period or length of the time-bucket is amplified with returned items, because available return information at a decision point is related to the frequency of the review. In this study, we analyze the effects of these two parameters over a multi-period inventory system where the length of a time horizon is fixed. Dynamic programming approach is used to calculate the optimal inventory positions. In dynamic programming, it is assumed that a fixed proportion of sold items are returned. Computational results are obtained to compare the effects of return information under different return proportions and period lengths. These results are used to conduct various analyses to explore the level of the advantage gained by using return information.

This thesis is supervised by
Prof. Nesim Erkip

Görkem Özdemir - HEURISTIC SOLUTION METHODS FOR PLANNING PROBLEMS IN WIRELESS MESH NETWORKS

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

23 July 2012, Monday at 10:00, Monday at 10:00
Room EA 409, Engineering Building, Bilkent University
Modeling and Optimization of Multi Scale Machining Operations
by

Görkem Özdemir
Department of Industrial Engineering
Bilkent University

Wireless Mesh Networks (WMNs) are wireless communication networks that consist of a finite number of radio nodes, in which a predetermined subset of nodes, called gateways, have wired connections to the Internet and the remaining ones transmit their traffic to a gateway node through the wireless media in a multi-hop fashion through the other nodes.

Wireless communication signals that propagate simultaneously within the same frequency band may interfere with one another at a receiving node and may therefore prevent successful transmission of data. In order to circumvent this problem, nodes on the network can be configured to receive and send signals in different time slots and through different frequency bands. Therefore, a transmission slot can be defined as a pair of a certain frequency band and a specific time slot. In addition, by adjusting the power level of a radio node, its transmission range can be modified.

Given a wireless mesh network with fixed node locations, demand rate at each node, and maximum power level for each node, we study the problem of carrying the traffic of each node to the Internet through the network. Our goal is to allocate capacities in proportion to the demand of each node in such a way that the minimum ratio is maximized. We propose a mixed integer linear programming (MILP) formulation to select a given number of gateway locations from among the nodes in the network, to determine the routing of the traffic of each node through the gateway nodes, to assign transmission slots to each node in order to ensure no interference among wireless signals, and to determine the transmission power levels. In our study, we adopt the more realistic physical interference model.

MILP formulation becomes computationally infeasible for larger instances, we propose several heuristic methods that can quickly find reasonably good solutions. The proposed heuristics simply approaches the problem in two steps. In both steps, easier MILP problems are solved. In the first step, a relaxed version of the proposed MILP is used to make routing decisions. Second step performs the transmission slot assignment with given results of the first step. We evaluate the performance of our methods on a number of data sets.

Keywords: Wireless Mesh Networks, Integer Programming, Gateway Selection, Routing, Transmission Slot Assignment.

This thesis is supervised by
Asst. Prof. Kağan Gökbayrak

Fevzi Yilmaz - Modeling and Optimization of Multi Scale Machining Operations

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

20 July 2012, Friday at 10:00
Room EA 202, Engineering Building, Bilkent University
Modeling and Optimization of Multi Scale Machining Operations
by

Fevzi Yilmaz
Department of Industrial Engineering
Bilkent University

A recent trend in the manufacturing industry is the production of high value added micro parts which are mainly used in biomedical and electronics industries. It is crucial to develop high precision machine tools and operate them with high efficiency through an intelligent selection of process parameters. However, the knowledge base for micro machining operations is quite limited compared to macro scale machining processes and needs to be further developed. This study focuses on a micro milling process that is capable of producing micro parts with three dimensional features made from engineering materials.

In this thesis, modeling and optimization of macro scale turning and micro scale milling operations have been considered. A well known multi pass turning optimization problem from the literature is used to benchmark the performance of the Particle Swarm Optimization (PSO) technique against nonlinear optimization algorithms. It is shown that acceptable results can be obtained in a short time through PSO. Then, micro scale milling operation is thoroughly investigated through experimental techniques where the influences of machining parameters on the process outputs (machining forces, surface quality, and tool life) have been investigated and factors affecting the process outputs are identified. A minimum unit cost optimization problem is formulated based on the pocketing operation, and machining strategies are proposed for different machining scenarios using the PSO approach. It is shown that significant reductions in machining time and unit cost can be obtained through process optimization.

This thesis is supervised by
Asst. Prof. Yigit Karpat

Muhammet Kolay - MINIMIZING THE TOTAL COMPLETION TIME IN A TWO STAGE FLOW SHOP WITH A SINGLE SETUP SERVER

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

19 July 2012, Thursday at 10:00
Room EA 202, Engineering Building, Bilkent University
MINIMIZING THE TOTAL COMPLETION TIME IN A TWO STAGE FLOW SHOP WITH A SINGLE SETUP SERVER
by

Muhammet Kolay
Department of Industrial Engineering
Bilkent University

In this thesis, we study a two stage flow shop problem with a single server. All jobs are available for processing at time zero.Processing of a job is preceded by a sequence independent setup operation on both machines. The setup and processing times of all jobs on the two machines are given. All setups are performed by the same server which can perform one setup at a time. Setups cannot be performed simultaneously with job processing on the same machine. Once the setup is completed for a job, processing can automatically progress without any further need for the server. Setup for a job may start on the second machine before that job finishes its processing on the first machine. Preemption of setup or processing operations is not allowed. A job is completed when it finishes processing on the second machine.The objective is to schedule the setup and processing operations on the two machines in such a way that the total completion time is minimized. This problem is known to be strongly NP-hard. We propose a new mixed integer programming formulation for small-sized instances and Variable Neighborhood Search (VNS) mechanisms for larger problems. We also develop several lower bounds to help assess the quality of heuristic solutions on large instances for which optimum solutions are not available. Experimental resultsindicate that the proposed heuristics provide reasonably effective solutions in a variety of instances and they are very efficient in terms of computational requirements.

Keywords: machine scheduling, flow shop, single setup server, total completion time, variable neighborhood search.

This thesis is supervised by
Prof. Ulku Gurler
Assoc. Prof. Mehmet Taner

Hunkar Toyoglu - MOBILE DISTRIBUTION NETWORK DESIGN

DEPARTMENT OF INDUSTRIAL ENGINEERING

17 July 2012 Tuesday 10:00
Room EA 409, Engineering Building, Bilkent University
MOBILE DISTRIBUTION NETWORK DESIGN
by

Hunkar Toyoglu
Turkish Land Forces

In this seminar we will present two studies that are related to mobile distribution network design. Main objective of the first study is to provide a decision support tool that can help plan ammunition distribution on the battlefield. We aim to speed up the solution time with the second study by providing a different mathematical formulation and developing a heuristic.

In the first study, we demonstrate through an extensive literature review that the existing models are not capable of handling the specifics of the ammunition distribution. To this end, we propose a novel three-layer commodity-flow location routing formulation that distributes multiple products, respects hard time windows, allows demand points to be supplied by more than one vehicle or depot, and locates facilities at two different layers. We derive several valid inequalities to speed up the solution time of our model, illustrate the performance of the model in several realistically sized scenarios, and report encouraging results. Finally, we introduce a dynamic model that designs the distribution system in consecutive time periods for the entire combat duration.

In the second study, we propose a different formulation approach than the common arc-based product-flow (Arc-BPF) approach in the literature. We associate product amounts to the nodes of the network resulting in a node-based product-flow (Node-BPF) formulation. Our main objective is to develop LRP models with fewer constraints and variables, which can be solved more efficiently. To introduce the proposed approach, we reformulate the ammunition distribution system (a complex four-index Arc-BPF LRP model) as a three-index Node-BPF model, which computationally outperforms the former. We then introduce a heuristic method.

Asligul Serasu Duran - Robust Airline Scheduling with Controllable Cruise Times and Chance Constraint

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

17 July 2012, Tuesday at 10:30
Room EA 409, Engineering Building, Bilkent University
Robust Airline Scheduling with Controllable Cruise Times and Chance Constraints
by

Asligul Serasu Duran
Department of Industrial Engineering
Bilkent University

This is a study on robust airline scheduling where flight block times are considered in two parts as cruise time and non-cruise time. Cruise times are controllable and non-cruise times are random variables. Cruise time controllability is used together with idle time insertion to handle uncertainty to guarantee passenger connection service levels while ensuring minimum costs. The nonlinearity of these cost functions are handled by representing them via second order conic inequalities. The uncertainty in non-cruise times are modeled through chance constraints on passenger connection service levels, which are expressed using second order conic inequalities using the closed form equations. Congestion levels of origin and destination airports are used to decide variability for each flight. Computational study shows exact solutions can be obtained by commercial solvers in seconds for a single hub schedule and in minutes for a 4-hub daily schedule of an US carrier.

This thesis is supervised by
Prof. Dr. Selim Akturk
Asst. Prof. Sinan Gurel

Feyza Guliz Sahinyazan - Mobile Blood Donation Logistics: Case for Turkish Red Crescent

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

13 July 2012, Friday at 10:00
Room EA 409, Engineering Building, Bilkent University
Mobile Blood Donation Logistics: Case for Turkish Red Crescent
by

Feyza Guliz Sahinyazan
Department of Industrial Engineering
Bilkent University

Blood transfusion is one of the most critical operations in various medical interventions. Currently, the only authorized way of securing the required blood for transfusion is through voluntary donations. For this reason, reorganizing blood donation operations to create an operable and efficient system is of utmost importance. Practice in several countries suggests that well designed mobile blood donation mechanisms have an important role in blood collection systems.

In this study, a mobile blood collection system is designed for Turkish Red Crescent (TRC) to increase blood collection levels. This design also takes into account operational costs as a second objective so as to aim the collection of large amounts of blood at reasonable cost. In the current system, TRC has bloodmobiles that perform dedicated tours to certain activities (fairs, college fests etc.), but at the end of each day, they bring the collected blood to a designated depot to prevent its spoilage. Considering blood’s considerably short shelf-life of 24 hrs, these dedicated tours may seem justifiable yet they are not efficient in terms of logistics costs. The proposed system consists of classic bloodmobiles and a new vehicle – called the shuttle – which visits the bloodmobiles in the field and transfers the collected blood to the blood centers, so that bloodmobiles can continue their tours without having to make daily returns to the depot.

A mathematical model is developed to determine the stops of bloodmobiles, the duration of each visit as well as the tours of the bloodmobiles and the shuttle. In the literature, a study that covers all these decisions does not exist. Therefore, a new extension of SVRP is defined, called SVRP with Integrated Tours. Also, a 2-stage IP based heuristic algorithm is developed for the same problem. The performances of these methodologies are tested on the data set obtained from past blood donation activities in Ankara. In addition, GIS data of the European part of Istanbul is used as a constructed test case. The Pareto set of optimum solutions is generated based on blood amounts and logistics costs, and finally a sensitivity analysis on some important design parameters is conducted.

This thesis is supervised by
Assoc. Prof. Bahar Yetis
Assoc. Prof. Mehmet Taner
Assoc. Prof. Savas Dayanik

Bharadwaj Kadiyala - Poisson disorder problem with control on costly observations

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

9 July 2012, Thursday at 13:30
Room EA 202, Engineering Building, Bilkent University
Poisson disorder problem with control on costly observations
by

Bharadwaj Kadiyala
Department of Industrial Engineering
Bilkent University

A Poisson process changes its rate at an unknown and unobservable time from one known value to another known value. Detecting the change time as quickly as possible in an optimal way is described in literature as the Poisson disorder problem. In our study, we provide a more realistic generalization of the disorder problem for Poisson process by introducing fixed and continuous costs for being able to observe the arrival process. As a result, in addition to finding the optimal alarm time, we also characterize an optimal way of observing the arrival process. We illustrate the structure of the solution spaces with the help of numerical examples.e


This thesis is supervised by
Assoc. Prof. Savas Dayanik

Pelin Cay - Organ Transplantation Logistics: Case for Turkey

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

29 June 2012, Friday at 10:00
Room EA 409, Engineering Building, Bilkent University
Organ Transplantation Logistics: Case for Turkey
by

Pelin Cay
Department of Industrial Engineering
Bilkent University

Organ transplantation is one of the fundamental and effective treatment techniques for the patients who have critical health problems. However, while 3,930 organs were transplanted to the patients in 2011, there still exist 20,954 people waiting for a suitable organ as of April 2012 in Turkey.Even though the exact numbers are different; the situation of well developed countries like USA is not very different in terms of organ donation and patient ratio. Thus; matching - defined as finding the best recipient for a donated organ- is very crucial for the overall organ transplantation process. There are mainly two different ways of matching in the applications: centralized and hierarchical method. In the centralized method, all patients and donors are monitored and matching is coordinated centrally. In the hierarchical method, the matching process is coordinated via a bottom-up hierarchy. The application in Turkey is also hierarchical, coordinated by 9 regional coordination centers and one national coordination center. Due to the nature of the matching application in Turkey, the cluster of each regional coordination center is crucial. There are many dynamics of the transplantation process like cold ischemia time -the duration that the organ survives without blood circulation-, operation times and specialized hospitals and teams.

In this thesis, we study the organ transplantation logistics mainly focusing on the Turkish application. We provide mathematical models that consider the problem specific requirements like ischemia time. We also consider two-mode transportation since airplanes or helicopters are also used widely in organ transportation. Finally, we also developed a simulation model to observe the hierarchical nature of the system and to evaluate the performance of the mathematical model outputs. Both mathematical model and simulation model outcomes based on Turkish datawere compared with actual regional coordination center locations of Turkey.

This thesis is supervised by
Assoc. Prof. Dr. Bahar Yetis

Ceyda Kirikci - Determination of Shelter Locations and Evacuation Routes for a Possible Earthquake in the City of Istanbul

DEPARTMENT OF INDUSTRIAL ENGINEERING
AN M.S. THESIS PRESENTATION

11 June 2012, Monday at 09:40 , Tuesday at 10:30
Room EA 202, Engineering Building, Bilkent University
Determination of Shelter Locations and Evacuation Routes for a Possible Earthquake in the City of Istanbuls
by

Ceyda Kirikci
Department of Industrial Engineering
Bilkent University

In this study, the location of disaster response and relief facilities in Istanbul is investigated in view of a possible earthquake. The problem under study is the problem of determining shelter locations and evacuation routes to be used in the aftermath of a possible earthquake. The objective is to evacuate people in limited time from their geographical locations to shelter locations via the street network of metropolitan Istanbul to minimize the average time spent on the way while obeying possible constraints on maximum allowed path lengths, shelter capacities, and road capacities. We propose two mathematical models for this purpose. Model 1 is a path based model in which possible paths that satisfy certain length restrictions are a priori determined by preprocessing the network data. Model 2 is an arc based model that uses the network data directly and creates the paths as a byproduct of the solution. Both models ensure that each demand node is served by at least one shelter node in accordance with road and shelter capacities. We examine the effects of the number of shelters to be opened, capacity restrictions on shelters as well as additional capacity restrictions on streets of the network that impose upper bounds on the total traffic that can be routed through a given street in a maximum allowed evacuation time. Related “what if” analysis is also provided to study how the evacuation process and evacuees are affected if critical arcs or nodes of the network are disabled.

This thesis is supervised by
Prof. Barbaros Tansel

Halit Uster - Integrated Topology Control and Routing in Wireless Sensor Network Design for Prolonged Network Lifetime

DEPARTMENT OF INDUSTRIAL ENGINEERING

Friday 3 May, 2012 - at:13:40
EA-409, Bilkent University
by

Halit Uster
Associate Professor
Department of Industrial and Systems Engineering
Texas A&M University (TAMU)

A Wireless Sensor Networks (WSN) can be deployed in inhospitable environments, difficult-to-reach terrains, and wild habitats such as war zones, oceans, the poles, deserts, and rain forests to monitor and observe natural, environmental, and climate- related phenomena including temperature, pressure, humidity, vibration, light, chemical and biological agents, etc. The most distinguishing characteristic of a WSN is the fact that its sensors have finite and non-renewable energy resources. In this talk, we consider integrated topology control and routing issues in data gathering WSNs and address the problem of prolonging network lifetime until next sensor deployment. We suggest a policy to be employed in each period of a deployment cycle and computationally examine its efficiency in prolonging network lifetime against two other commonly employed approaches. We present mathematical models and a general efficient solution procedurethat is applicable to all the three policies. We determine that the proposed policy has attractive properties and exhibits very good performance in extending network lifetime. (Research supported by National Science Foundation under grant CMMI-0428831).
(Joint work with Hui Lin)

Halit Uster,Texas A&M University (TAMU)

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