# Lisans Programı Araştırma Projeleri

2020-2021

 Student Name Project Title Advisor(s) Fahaar Mansoor Pirani An Estimated Cutting Angle Algorithm to Solve Difference of Two Convex (DC) Functions Firdevs Ulus Abstract Convex Optimization is an increasingly important sub-class of mathematical optimization in Operations Research that minimizes convex functions over convex sets. It has a wide range of applications such as EOQ models in production planning problems, Markowitz Portfolio problem in the finance, and Chebyshev Center problem. Moreover, a different problem has evolved from convex optimization that includes minimizing the difference of two convex functions over a convex set. The problem here is that the difference of two convex functions (DC function) may not necessarily be a convex function. These classes of problems are known as DC optimization (or DC programming). Until now, there have been no accurate algorithms to solve these problems except for some special cases. However, estimation algorithms give near-optimal solutions to these problems. In this project, we implement an estimated cutting angle algorithm to obtain an estimated solution to the problem. The algorithm adds a linear piecewise function to the first component of the DC function iteratively until a near-optimal solution is obtained. The algorithm is currently being applied to univariate functions. Later on, this algorithm will be applied to functions defined on R^n. Banu Tiryaki Codon Optimization: A Network Modelling Approach Oya Ekin Karaşan, Alper Şen Abstract Gene synthesis is mainly creating codon sequences. However, the codon usage is biased meaning that not all the codons observed equally likely in the encoding of an amino acid which results different gene expression levels. There are some metrics that are used to represent the level of gene expression; one of them is the Codon Adaptation Index (CAI) and another one is the Codon Pair Bias (CPB). The objective of Codon Optimization is to find the optimal codon sequence with the maximum CAI and CPB values to improve the gene expression level. We formulated this problem as a network problem. We focused on finding the sequential longest path on a network constructed by the codons as the nodes and CPB values as the arc costs. Since this is a multi-objective problem, we formulated our model with an additional constraint to ensure the total minimum CAI value of the sequence. Ecenur Oğuz Implementation of A Polynomial Solution for The Quantile Assignment Problem Mustafa Çelebi Pınar Abstract Quantile assignment problem will be introduced and its importance will be explained. It was proven that the problem was polynomially solvable. I have implemented an algorithm for the solution with Python. The logic of the algorithm will be explained and further goals will be mentioned in the presentation. Beste Fırat A Streamlined Process Planning Approach for Additive Manufacturing Service Bureaus Yiğit Karpat Abstract Manufacturing as a service (MaaS) is defined as the shared use of manufacturing infrastructure to produce parts. Such services allow some individual users and small scale companies to gain access to new technologies such as additive manufacturing. In a conventional way, customers send their part designs, wait for a quotation and manufacturability analysis on their designs. Manufacturing of the parts start after some iterations between the customer and the manufacturer, which inevitably increases the lead time and cost of the parts. With the rise of digitization, in recent years some MaaS companies have significantly improved their business models where customers receive instant quotations after they upload their part designs to a server. Some of the companies do not even own any manufacturing infrastructure but manage a network of manufacturers acting as facilitators between customers and manufacturers. In this project, I will first investigate the industrial engineering approaches employed in such systems. Then, I plan to develop a streamlined system for the operations of an additive manufacturing service bureau. Merve Ayyıldız Migratory Beekeeping: An Interesting Application of Selective Vehicle Routing Problem Bahar Yetiş Kara Abstract Beekeeping has attracted worldwide attention due to its contribution to economy and biodiversity. In light of these, migratory beekeeping has been widely adopted as a highly efficient technique since it remarkably increases incomes by moving bee colonies to places where forage is abundant at certain times of the year. However, due to the lack of a general orientation plan, migratory beekeepers following experimental routes often experience unexpected deviations and losses when faced with problems such as nectar resource capacities and the production of bee products. This study aims to provide an exact model to optimize the routes for migratory beekeepers by taking into account nectar source allocation, time decisions, production of bee products, and transportation. Since visiting all potential stops is not practical in our case due to some limitations, we approached migratory beekeeping routing problem as an application of Selective Vehicle Routing Problem. Irmak Özdemir Logistics Management of Half-Mobile Testing Booths for COVID-19 Bahar Yetiş Kara Abstract Due to the recognized spread of COVID-19 as a pandemic on 11 March 2020, the health organizations and governments try to develop distinct testing policies around the World. The reason for adopting different methods is trying to reach potentially sick persons as soon as possible in order to treat them earlier, therefore preventing the virus from spreading. The focus of this study is to investigate successful COVID-19 testing methods around the World. Then, a model for the implementation of the testing method will be proposed in order to efficiently increase the COVID-19 testing rate by reaching as many testees as possible while using the scarce resources – testing kits, the protective pieces of equipment that the health personnel wear. While maximizing the number of viral tests resulted, the total distance among the booths will also be considered. That is because all the samples collected from the booths are to be resulted in a designated testing center despite being near the hospital areas. The testing implementations that authorities acquire may come out as traditional, half-mobile, or mobile. The proposed model is to be tested with relevant data and a literature review on the related OR studies will be given. In the literature, the proposed problem is mostly related to the Selective Vehicle Routing Problem, Covering Tour Problem, and Team Orienteering Problem.

2019-2020

 Student Name Project Title Advisor(s) Tolunay Alankaya Regression Analysis of Factors Affecting Employee Turnover Savaş Dayanık Abstract In this study, the reasons for employee turnover have been investigated. The aim was, besides, predicting the probability of an individual’s flight risk, more importantly measuring the covariates’ effects on employee turnover. Survival analysis has conducted for the problem and it gave some insights into the effects of covariates. Although we could test the significance of the covariates’ effects by using Cox proportional hazards regression model, a further investigation had to be done to finding the interaction effects of the covariates. Cox regression model has vastly used in survival analysis of many different subjects and a decent amount of papers are available in the literature which is dealing with employee turnover problem. Even though there are many types of research using that method, a challenge has arisen due to the sample size of our data. Unlike other studies, the sample size of our data has increased in years. For example, we could not easily compute the hazard rates by looking at a particular time interval since there were always new employees who have started to work for the company in that time interval. Besides that, our data is problematic in terms of missing values. For handling this problem, multiple imputation methods have used for some continuous variables in the data set. After the data has tidied, survival analysis has conducted for estimating the effects of covariates and the results of it have compared with the results of traditional methods like logistic regression. Mert Cüceloğlu Description of the Minimizers of Least Absolute Deviations Regularized with l0-norm Mustafa Pınar Abstract Using the input data points in a matrix A to express or predict the output data points given as a vector d, while using as few as possible variables, is the main focus of the study. For that purpose, an objective function is adopted where the loss function is Least Absolute Deviations and it is regularized with the sparsity of the optimal vector, i.e., the l0-norm. We shall investigate the local and global minimizers of the objective function, as well as sufficient and necessary conditions for a local minimizer to be strict. Sparsity of the vectors will be studied through bounds on regularization parameter. The findings will also be supported with numerical examples. Berkay Özşeker Predicting Employees Risk of Leaving a Company Savaş Dayanık Abstract Organizing the employee structure of a company is crucial in terms of many aspects such as, sustainability of the working environment, quality of the deliverables etc. For having a robust organizational structure, a company should know when an employee will leave so that they could be able to find another candidate for the empty position in a time period that will not affect the companies working environment. From the employee’s perspective deciding on leaving his/her position is being affected by different parameters. Having a child, prior work history, increase amount in the salary etc. Aim of this study is to predict the leaving risk of an employee with a survival analysis approach and determining related parameters and their effects on the leaving risk. For the survival analysis, an Artificial Neural Network structure which has an output layer of Cox Regression Node will be evaluated and implemented accordingly. Furthermore, some approaches used in the field of genomics will also be considered. Selin Sütçü Implementation of an Algorithm for Finding a Complete Matching with a Single Restriction Mustafa Pınar Abstract The main aim of this study is the implementation of an algorithm for finding a complete matching with a single edge subset restriction. Finding a maximum cardinality matching in graphs is a very well-known tractable problem. The variation of restricting the matching so that it uses only a limited number of edges from specified subsets results in an NP-complete problem. When the restriction is only to a single subset, the resulting problem can still be solved in polynomial time. This specific problem is the focus under the current study, and it has applications in the field of image processing. In the beginning of the study, we shall focus on implementing a known algorithm for bipartite graphs. The algorithm solves a maximum flow with minimum cost making use of several shortest path solutions in the process. Later, we shall extend this algorithm to non-bipartite graphs. The performance of our algorithms will be tested against integer programming models available for solving the restricted matching problems. Berkay Taşçı Bayesian Quality Control at Points of Sales Savaş Dayanık Abstract A prominent food company wants a point of sale quality control mechanism to monitor the quality of products on the shelves. After leaving the factories, products are prone to mishandling during transportation and stocking at warehouses. We would like to develop a Bayesian model of quality failure process of products traveling through a hierarchical network of replenishment channels from factories to stores. Then we will use the model to design effective points of sales sampling strategies to maintain high quality products at the shelves. Didar Uslu Identification and Validation of Employee Turnover Process Factors in a Manufacturing Company with Panel Data Models Savaş Dayanık Abstract Various factors play key roles in the process of leaving a job for an employee in ways of quitting, resigning or being dismissed. The employees are perceived as investments and resources for companies with the budget and effort provided in the recruitment and onboarding processes so the leaving process is a loss for the company in terms of resource which needs to be held under control and monitored. In this investigation, the main reasons behind resignation and dismissal of employees of a leading manufacturing company is examined to monitor employee commitment and predict the probability of leaving. The models are built on panel data that consists of variables such as personal, performance, education, organizational, supervisor information with marital status and commitment level of employees. Various tools and packages in the R software environment are used to build the survival and effects models that find the correlation and significance of variables with the risk analysis of leaving. The results aim to provide insight on employee portfolio of the company and suggest improvements to the recruitment and management strategy of the company’s human resources department. Fatih Selim Aktaş Optimal Sparse Solutions to Linear Systems with l1 Loss by Implicit Enumeration Mustafa Pınar Abstract Computing sparse solutions to linear systems is an ubiquitous problem in several fields such as machine learning, signal and image processing, information theory. The objective of the present project is to develop a Python code implementing an implicit enumeration algorithm to find optimal solutions to the NP-hard problem `min_x ||Ax - b||_1 s.t. number of nonzeroes(x) <= s,` for a given sparsity level s. We assume A is a m x n real matrix where m > n hence, the linear system is over determined. Most previous research efforts concentrated on the conditions under which solving the relaxation of the problem would yield a sparse solution when matrix A is underdetermined. We previously proposed another algorithm to find sparse solutions to overdetermined linear systems where l2 loss was used. Berkay Becü An Algorithm to Find Extreme Supported Non-Dominated Points of Discrete Multi Objective Optimization Problems Firdevs Ulus Abstract We consider multiobjective integer linear optimization problems and propose a modified parametric simplex algorithm for finding the set of all extreme supported nondominated points. The parametric simplex method is used for finding efficient solutions to the LP-relaxation and a corresponding weight space decomposition. In order to find the supported non-dominated points for the discrete problem, we employ cutting plane methods within the parametric simplex algorithm. The algorithm is implemented in MATLAB and tested on illustrative examples. Different variants of the algorithm will be considered, and further computational tests will be conducted. Şebnem Manolya Demir A New Selective Location Routing Problem: Educational Services for Seasonal Workers Bahat Yetiş Kara Abstract There are approximately 155 thousand seasonal workers in Turkey who temporarily migrate from their hometowns during the harvest seasons. Children of these workers mostly travel with their families, and the ones attending primary or secondary schools miss a long period of the school year. To prevent this, Turkish Ministry of Education assigns these children to a school in the migrated city. If the city schools are already operating at full capacity, or distant from the migrated harvesting field, then the ministry can construct a prefabricated school in the district. In case the children of a district are assigned to a central school, there are two transportation options. If the central school is located within two kilometers of the district, children can walk to the school; otherwise, they should be transported with a school bus. This application requires a combination of selective routing and location routing problems. In this study, we develop such a mathematical model that determines the assignments to the central schools and the school bus routes, as well as the locations of prefabricated schools. We tested the performance of our model using the data from the Southeastern provinces of Turkey that receive the largest influx of seasonal workers within the country. While this problem is inspired by the seasonal workers of Turkey, our model can be beneficial to increase the school attendance rates in other developing countries, especially for the sparsely populated rural regions without adequate schooling facilities or teaching staff. Azra Çağlasu Gürkan Time Dependent Selective Routing Problems with Time Window Bahar Yetiş Abstract Although time dependency of selective routing problems did not have studied in the literature, there are several real-life applications which show that a problem may be depend on time in terms of obtained benefit when a node is visited. This paper aims to examine selective routing problems in terms of time dependency issue by considering time windows and a mathematical model for a time dependent selective routing problem with time window is built. Egemen Elver Fairness in Knapsack Problems Özlem Karsu Abstract We consider binary knapsack problems where the decision maker has equity concerns and items have benefits in multiple categories. We first consider the deterministic problem, where all parameters are known. To ensure a balanced allocation of benefit over multiple categories, we minimize the sum of squared regrets over them and discuss alternative nonlinear and linear formulations for the square regret minimizing problem. We then perform extensive computational experiments to compare these formulations. We will now extend this analysis to see the effects of increasing parameter p on the regret allocations. Furthermore, we will consider a stochastic environment, where the item costs are uncertain. For this setting, we propose a robust programming extension so as to guarantee an equitable benefit allocation even under the worst scenario. Seyit Ulutaş Solution Algorithms for Equitable Optimization Problems Özlem Karsu Abstract We consider equitable optimization problems, which are multiobjective optimization problems involving symmetry and propose exact solution approaches that guarantee finding the whole set of equitably non-dominated solutions. We first propose an algorithm for the biobjective setting. This algorithm is based on iteratively solving two-stage scalarization models, the first stage of which maximizes the total, while the second stage ensures that the resulting solution is equitably non-dominated. We then consider extensions to three objective settings; design two algorithms and observe their performance by conducting computational experiments. For future work, we plan to explore different scalarizations and modify existing algorithms used for classical multiobjective optimization problems such that they are implementable in equitable settings.

2018-2019

 Student Name Project Title Advisor(s) Tuana Terkin Multi-Period Joint Ordering and Inspection Policy Decisions under Stochastic Demand Nesim K. Erkip Abstract In this research, we want to develop a model for the multi-period joint ordering and incoming lot inspection decisions for an item in a manufacturing context where there is stationary stochastic demand. It is presumed that there is uncertainty over the quality of the raw-material supply which consequently affects the yield for the production of the end-product. Possible incoming lot inspection decisions are “%100 inspection”, “no inspection” or “inspecting based on a specific acceptance sampling plan”. Order placements and deliveries from supplier are periodic and ordering policy is order-up-to policy. Benefit of using such a joint model is to be investigated. Seyit Emre Düzoylum Dynamic mean-variance problem: recovering time-consistency Çağın Ararat Abstract Introduced by Markowitz in 1952, obtaining a portfolio which maximizes the expected return while minimizing the risk is one of the fundamental problems of financial mathematics. The original Markowitz problem utilizes variance for the measure of risk. However, due to its insufficiency in terms of some key aspects, more adequate risk measures have found their place in the literature. Resulting from the multiple objectives of the problem, in most of the literature it has been scalarized and formulated as a single-objective optimization problem. Furthermore, due to its multi-period nature, it has been usually referred as dynamic Markowitz problem in the case of variance, or dynamic mean-risk problem otherwise. However it is well-known that the scalarized versions of mean-variance multi-objective problem does not satisfy Bellman’s principle of optimality, thus conventional methods are not appropriate for time-consistency of solutions.Recent studies on the subject have introduced new approaches to the mean-risk problem and been able to obtain time consistent solutions. One that we are most interested in, uses a coherent dynamic risk measure, proposes not scalarizing the problem but preserving its multi-objective nature and design it as a vector optimization problem (VOP), whose objective function gets extended to a set-valued function. As a result time-consistent solution to the mean-risk problem was obtained.The main objective of our study is to obtain time consistent solutions to the original, mean-variance problem by applying a similar approach and formulating the problem as a VOP. However having the variance for the measure of risk compared to a dynamic risk measure, results with some critical problems arising. The most important one is time-inconsistency of variance due to its properties. Therefore one would not be able to use the same approach as mentioned and obtain time-consistent solutions. For this reason, we are investigating a novel formulation to the problem and expecting to attain time-consistent solutions through this novel formulation combined with the VOP approach. Alper Yılmaz Input Models for Energy Commitment Decisions of a Hybrid Energy System with Pumped Hydro Storage Ayşe Selin Kocaman and Emre Nadar Abstract Systems that utilize renewable energy have the disadvantage of dealing with sources that are often irregular and intermittent. Hybrid systems aim to circumvent this issue by using various renewable energy sources and energy storage simultaneously. One such system is a hybrid system that implements a pumped-back pumped hydro storage (PHES) and a renewable energy production plant together with the objective of total profit maximization from energy commitment decisions. Formulation of this optimization problem, involving Markov decision processes and multi-stage stochastic programs, requires several input models: the electricity pricing model, the wind speed model, the streamflow model, and the solar radiation model. The aim of this project is to find the proper input models that will be used in the main profit maximization problem that provide the best representation of real life data by utilizing data analysis and other mathematical modelling techniques. Mert Çetin Heuristic Algorithm for Drone-Aided Delivery Problem Bahar Yetiş Kara Abstract In recent years, unmanned aerial vehicles (aka. drones) have become increasingly popular in commercial sector, so that delivering packages using drones along with the traditional trucks started to gain attention for logistics companies. Those companies aim to reduce their overall logistics cost, delivery times, energy consumption and further to be able to deliver to places whose transportation infrastructure is inadequate. For that purpose, a two-echelon delivery system where both trucks and drones are involved, is constructed and modeled. However, computing times grow rapidly when number of nodes increase. Thus, the goal of this study is to develop a heuristic algorithm to drone delivery problem. Deniz Şimşek An Objective Space Based Solution Approach for Multi-Objective Integer Programming Problem Firdevs Ulus and Özlem Karsu Abstract We consider multi-objective integer programming problems. We aim to design an objective space based solution algorithm that generates the whole set of non-dominated solutions. We will first consider three objective integer programming problems and then extend the algorithm for any number of objectives. A number of variants will be developed and compared through computational experiments. Duygu Söylemez Split Order Problem Alper Şen and Oya Karaşan Abstract One of the major problems that online retailers face is the split order problem. A split order occur when a retailer cannot satisfy the order by sending all the SKUs in the order in one shipment. The reason behind this is basically the unavailability of all the SKUs in the same warehouse. Because of the limited capacities of the warehouses, it is not possible to have all types of SKUs in every warehouse. The aim of the project is to minimize the number of the split orders to reduce extra packaging & shipment costs, and the delivery time. In the literature, this problem is studied in two different ways. In the first approach, it is assumed that the unit-based capacity of warehouses is unlimited, and it is only necessary to find the most appropriate assortment of SKU types for each warehouse. In the second approach, the capacity is thought as the unit-based capacity, and it is required to find the optimum amount for each SKU to stock in each warehouse. However, both versions of the problem are NP-hard. For this reason, previous studies are based on heuristic approaches. In this research project, it is aimed to come up with an optimization model which can be used to minimize the split order problem with a great number of SKUs for any version of the problem, or construct heuristic approaches that can compete with the existing literature. İrem Nur Keskin Utilizing Double Description Method in Benson’s Algorithm Firdevs Ulus Abstract Benson’s approximation algorithm is designed to solve convex vector optimization problems in the sense that it approximates the whole Pareto frontier. At each iteration, the algorithm solves a vertex enumeration and a convex optimization problem. Vertex enumeration is used to find the vertex representation of a polyhedron (current outer approximation of the Pareto frontier) given by its half-space representation. Convex optimization problem is solved for each vertex of the current polyhedron. It finds the minimum ‘distance’ between the vertex and the Pareto frontier through a given direction, which is fixed through the algorithm. The first objective of this project is to implement a vertex enumeration algorithm that uses the double-description method, to employ it as a subroutine within Benson’s algorithm, and to examine the effect of the new subroutine on it. Secondly, by using the structure of the new vertex enumeration subroutine, we aim to modify the direction parameter of the convex optimization problem that is solved in each iteration and to analyze the performance of this variant of the algorithm.

2017-2018

2016 Fall

2015 Spring

 Student Name Project Title Advisor(s) Eren Özbay Disaster Management under Multi-Hazard Scenarios Bahar Yetiş Kara Abstract In some of the cases disasters follows each other (e.g. a tsunami after an earthquake) or there are large-scale disasters in neighbourhood of each other if not correlated. This project aims to manage the distribution of resources among separate disasters to reduce the firefighting in aforementioned cases using multiple-criteria decision making methods. Buse Eylül Oruç Damage Assessment after an Earthquake Bahar Yetiş Kara Abstract Post-Earthquake damage assessment system will be designed with the objective of collecting information about the disaster affected regions and the roads that have great importance. This design will also take into account the budget/vehicles allocated to the assessment and the inspection time. Aim is to create routes for information collecting vehicles that visit as many as disaster affected regions with high priorities by using the important connections with respect to time and budget constraints. Alp Süngü An extension of the transportation consolidation project for e-tailing Nesim K. Erkip Abstract In the project we consider a transportation consolidation problem for e-retailer companies that has two types of services: regular and premium. Regular service basically guarantees a delivery time. However the premium service has negligible delivery time where it has a close enough inventory to the customer. When the shipment decision is made, the truck is filled with both regular customer orders and the selected items for the inventory. Our goal is to create simulation models which reflect the actual system and implement different policies in order to determine the most beneficial one for the company concerns. Başak Erman Integration of production and transportation cycles in a carbon-sensitive environment with multiple transportation modes Nesim K. Erkip, Ülkü Gürler Abstract Reduction in carbon emissions gained importance in recent years as sensitivity on environmental issues has increased. Companies are expected to regard environmental objectives as well as the economic ones while taking decisions. To motivate them, different carbon emission regulations are implemented such as carbon cap and taxation. In this research we analyze a supply chain between the manufacturer and the retailer under the presence of different carbon emission restrictions. Specifically, we study the integration of production and transportation intervals under a carbon sensitive environment. We focus on a system where multiple transportation modes are available and it is possible to switch modes. Under this consideration, we analyze inventory, production and transportation operations. Hale Erkan Management of airport congestion through rescheduling using fairness measures Nesim K. Erkip, Özge Şafak Abstract This project is a continuation of the IE490 project we worked on. The study is based on managing congestion caused by the delayed flights at airports. Due to capacity, mechanical constraints and other reasons flights have to face important amounts of delays. These delays may result in more critical problems when an airport has to operate as in its peak almost all the time. Air traffic flow management approaches are utilized to reduce the effects of congestion for the airlines and passengers, as well as providing safe, efficient and effective solutions to these problems occurring as a result of delays. We propose an approach that reschedules flights for an airport. One important issue in decisions to alter schedules is fairness. It would not be fair to think about all flights as identical and minimize the total delay. Each flight has different number of passengers, belongs to a different airline and has connections at different times. From this angle, we concentrate on fairness measurements, as well as the trade-off between more standard performance measures and fairness. We model the rescheduling problem as a mixed-integer programming model. We consider a general formulation which can be utilized by an airline, by a central authority (air traffic control), or by any agent that may have a part in the fairness of new schedule. We run our model for a real-life case represented by limited data and compare with current practices reported in the literature. Ayşenur Karagöz Application of Risk-Averse Support Vector Machines (SVM) to Bioinformatics Decision- Making Özlem Çavuş Abstract In this study, we consider the application of risk-averse SVM to classify the patients as having cancer or not. Although SVM classification is closely related to structural risk minimization, in regular SVM, misclassification error considerably increases for noisy and corrupt data due to overfitting (Tsyurmasto et al., 2014). Risk-averse SVM is able to prevent overfitting in the training data, therefore, gives better results in the test data. The aim of this study is to apply risk-averse SVM with different risk measures and different degrees of risk-aversion to colon cancer data and to compare it with regular SVM. T. Deniz Aktürk An analysis of the portfolio optimization problem for the Turkish fund market under different risk measures Oya Ekin Karaşan, Çağın Ararat Abstract This study compares the effectiveness of different risk measures in the selection of efficient portfolios. Optimal multi-asset fund portfolios based on the Turkish fund market data are evaluated under certain coherent risk measures. In order to find such portfolios, these risk measures are used in the risk constraint of the portfolio optimization problem which is formulated as a mixed integer nonlinear program. Lastly, the report aims to maximize expected return on portfolios formed in the Turkish Fund Market domain and also construct a market analysis in order to determine and show underlying correlations between certain asset groups. Harun Avcı Approximate Dynamic Programming for Partially Observed Markov Decision Process Discretization Kağan Gökbayrak, Emre Nadar Abstract We study the inventory replenishment problem in a single-item system with nonstationary demand and partial information. The probability distribution of the demand undergoes Markovian transitions over time. However, the state of the distribution is only partially observed through the actual demand values. We model the problem as a partially observed Markov decision process (POMDP) with continuous state space for beliefs. We then consider an average cost in infinite horizon problem with full backordering, and linear holding and backorder costs. We use approximate dynamic programming via discretization of the state space, seeking cost- effective, and computationally efficient, heuristic policies. Selin Özbek Optimal risk reduction strategies for a system with degrading components Çağın Ararat, Ülkü Gürler Abstract Performance of a system can be measured by the performance of its subsystems or components. Time to failure of a component determines its performance. Reliability of a system is defined as the probability of working (not failing) during a given time interval ([0, t]). State of a component of the system is described by a random variable. In the binary case, this random variable takes values 1 (not failing until time t) or 0 (otherwise). Structure function of a system is also a random variable (a function of the component states) that reveals the condition or the state of the system. If the component states are described by binary variables, then the structure function also takes only 1 or 0 values corresponding to working or failure condition. The aim of this research project is twofold: First we want to describe the state of a system of components, when the component states are described by discrete random variables that take finitely possible values. This modeling allows describing the state of a component in more precision and is more realistic to describe the real world performances of physical and nonphysical systems as they tend to degrade before failing. In this case, multi state representation of components, subsystems and systems becomes useful and necessary for a better analysis. Under this more realistic assumption the first aim is to describe the structure function of the system and find its probability distribution. The second aim of our project is to introduce alternative risk measures for such systems and find optimal risk reduction policies that provide a desired improvement in the risk measures with an optimal cost. Ahmet Onur Akgül Polynomial Approximation in Inventory Control Kağan Gökbayrak, Emre Nadar Abstract In this project, an inventory replenish system with single item, non-stationary demand and partial observation will be examined. Demand will depend on Markovian transitions over time. We will develop polynomial approximation algorithm to find cost-effective basestock policies.

2015 Fall

2014 Spring

 Student Name Project Title Advisor(s) Kürşat Tosun Valuation of Wind Farm Operations with Energy Storage Systems Alp Akçay & Ayşe Selin Kocaman Abstract Due to the variability and intermittency of wind power, operating a wind power plant has become a significant challenge for electricity generating companies. Energy storage system (ESS) has been considered in the literature as an effective supplement to the wind power plants to smooth wind power fluctuations. In this study, we propose a dynamic programming approach which accounts the Turkish Electricity Market constraints and uncertainty in wind power and price. In this problem, our aim is to find the optimal daily operation strategy for ESS integrated wind power plant which maximizes its daily profit. Ece Çiğdem Wind Power Estimation in Turkey Using Hybrid Forecasting Techniques Ayşe Selin Kocaman Abstract Wind energy has become a crucial field of study, especially in the countries having high potential of wind energy production such as Turkey. In Turkey, the installed wind energy capacity is just 3.11 percent of the total capacity; however Turkey has plans for future to increase the usage of the wind energy and to fulfill its 48 GW potential. In order to integrate wind energy into the large power systems, and to take the advantage of wind power production, it is important to forecast the expected hourly wind speed accurately. There are several forecasting techniques such as meteorological, traditional and artificial intelligence methods that have its particular advantages and disadvantages. Generally, meteorological models do not give accurate results, traditional methods assume the future values of time series have linear relationship with current and past values and some artificial intelligence models possess their own defects and drawbacks. In the view of the limitations of traditional and artificial intelligence techniques, this paper proposes a hybrid modeling approach to forecast hourly wind speed. By using hybrid modeling system, different methods are combined in order to improve forecasting quality compared to the respective single techniques. With this motivation, the 81 province of Turkey’s wind speed data for the years between 2009 and 2014 are obtained from TÜMAŞ (Türkiye Meteorolojik Veri Arşiv ve Yönetim Sistemi) and started to be analyzed. Data are also used to establish a hybrid forecasting model for the future hourly wind speed. Özlem Yılmaz A Risk-Averse Approach for Planning of a Hybrid Renewable Energy System Özlem Çavuş & Ayşe Selin Kocaman Abstract A hybrid energy system that consists of solar panels, hydropower stations, diesel generators (to be used as an expensive backup source) and transmission lines is proposed in the Ph.D. dissertation of Dr. Ayşe Selin Kocaman with a case study in India. (Essays on Infrastructure Design and Planning for Clean Energy Systems by Kocaman, Ayşe Selin, Ph.D., Columbia University, 2014, 224 pages; AAT 3628950) Aim of that study is to design the power stations and the transmission lines in a most cost effective way while satisfying the energy demand. Due to intermittency of renewable energy sources, such as variability in stream flows, this system is modeled as a two-stage stochastic linear programming problem. However, in that study the decision makers are assumed to be risk-neutral and since the system construction requires large amount of investment, it is crucial to consider the risk-averse case. Therefore, in our research, we would like to add conditional value at risk measure (CVaR) which focuses on minimizing the expected value of extreme costs. Since large number of scenarios will be considered in this study, the L-shaped method will be used for solving the risk-averse model. Büşra Ökten Multi-Objective Optimization of Grid-Connected Decentralized Power Systems Ayşe Selin Kocaman & Özlem Karsu Abstract In the last years, the increasing energy demand and concerns about the environment have led decision makers to find new ways of satisfying the needs. One of the approaches that has gained popularity recently is switching from centralized to decentralized power systems. However, this transition could be difficult when conflicting objectives are involved. On one hand, burning fossil fuels provide required energy at a low cost, yet with high CO2 emissions. On the other hand decentralized systems using renewable energy sources satisfy the demand with little impact to the environment, but the investment and operation cost of the system are high due to advanced technologies. In order to deal with the multi-objective optimization problem and guide the decision makers, a two-stage stochastic optimization model has been developed. Total cost of the system is trying to be minimized while CO2 emission level is kept at a certain level by using ɛ-constraint method. Finally the model proposed here is simulated by using data from Turkey and the results are discussed. Betül Ergin New-Product Launch Timing when Demand Depends on the Product Life Cycle Alp Akçay & Emre Nadar Abstract The replacement of an existing product with a new one presents many practical challenges. In this research, we consider the problem of timing a new product launch when there is a transition period in which the new and the old product types can be offered simultaneously; i.e., a product is not necessarily replaced by its next generation counterpart immediately. Assuming that the demand for each product generation is driven by where the product is in its life cycle, we aim to determine the optimal delay in new-product introduction using a dynamic programming formulation of the problem. Emine İrem Akçakuş Formulations for Hub Arc Location Problems Bahar Yetiş Kara & Meltem Peker Abstract Hubs are special facilities which connect a set of interacting nodes and serve as switching points. Hub location problem deals with the allocation of nodes to hubs in a way that total cost or distance is minimized. Campbell et al. (2005) introduces a new approach to this problem by locating hub arcs instead of hubs and proposes models for four special cases of q-hub arc location problem. In this research, we aim to construct better models for q-hub arc location problem and we also plan to analyze the q-hub arc covering problem which has not been defined in the literature yet. Ömer Burak Kınay Hedging Uncertainty in Preventive Disaster Relief: Optimization Models and Algorithms Bahar Yetiş Kara Abstract A disaster is defined as “a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources” by The International Federation of Red Cross and Red Crescent Societies. The problem that consists of selecting temporary shelter areas for people who lost their homes after a disaster is one of the crucial problems in disaster relief. This problem is known in the literature as the shelter site location problem. The selection of the eligible sites for shelter areas is customarily done a priori to a natural disaster. However, depending on the type and severity of the disaster, the amount of sheltering needed may vary significantly. For that reason, it is important to hedge uncertainty when such a selection is defined. This work aims at capturing uncertainty in the shelter site location problem and it is aimed to propose a chance-constrained model in which the probabilistic constraints are associated with the service level as well as with the minimum utilization rate that is required for making use of a shelter site. Pırıl Cantürk Developing a Heuristic Method for Hierarchical Multimodal Hub Location Problem on a R-S-S Network Bahar Yetiş Kara & Okan Dükkancı Abstract A logistics hub is a facility that is designated to deal with activities related to organization, consolidation and transshipment of goods in many-to-many distribution systems. The hub location problem in network setting aims to find the locations of hub nodes and allocation of the demand (non-hub) nodes to these located hub nodes. The goal of the project is developing a heuristic method for a hierarchical multimodal hub location problem with a ring(s)-star-star (R- S-S) structure in a cargo delivery network. Sinan Türkmen A Decision Support System for Medical Service Planning After an Earthquake Bahar Yetiş Kara Abstract This work proposes an analysis framework to determine the location of field hospitals and the distribution of the patients to the existing roads for medical service planning through earthquake. Two mathematical models are used in order to find the most usable field hospitals and the number of patients that goes to hospitals through specified roads. Apart from the similar researches, this study uses an online application that takes the instant data from the real world which involves the number of patients, the existing and field hospital capacities and in case of insufficiency, the number of patients that uses air-transportation. The objectives of the models are to minimize the total travel time which uses a weighted parameter in order to compensate for the non-served patients. The research is studied for the Istanbul data and can be used for any city. The first mathematical model tries to find the most suitable field hospital locations in terms of accessibility. After finding the field hospital locations, an up-to-date data about hospital capacities and number of patients is taken from the responsible posts via an on- line application. Arc-Gis is used in order to calculate the average travel time between locations and this data is used in second mathematical model to determine the number of patients going from a patient location to a hospital. The model is solved continuously in order to create a more practical advises for decision takers. C-plex and Excel is used for mathematical model solutions. This framework is used in order to form a decision support system for transportation problems in medical service planning after an earthquake.

2014 Fall

 Student Name Project Title Advisor(s) Öykü Ahıpaşaoğlu Setting Staffing Levels at Concession Stands in Movie Theaters Nesim Erkip Abstract In the concession stands in movie theatres, it is possible to observe high demands (peaks) in short amount of times, especially before the movies and during the breaks. The aim of this research is to turn this time-varying demand into staffing requirements and schedule the shifts by considering the tradeoff between profit and a certain level of service at the same time. A queueing and an optimization model will be used to solve the problem. Although the research is originally based on the concession stands, it can be also applied to other service areas as well. Yağmur Meray Algül Escaping Local Optima in Coverage Control Problems Kağan Gökbayrak Abstract We address coverage control problems for multi-agent systems in a mission space with obstacles. These agents, sensor nodes, operate in a distributed fashion employing only their local information. These problems are non-convex, hence suffer from multiple local optima. In order to escape any local optimum, a boosting function approach that modifies the gradient values is proposed. In this approach, the main idea is to alter the local objective function whenever the equilibrium is reach to explore poorly covered areas of the mission space. Different families of boosting functions are simulated. Based on the simulation results, the performance of families are compared in terms of improving the objective function and the number of iterations. The effects of parameter values, number of nodes and obstacle configurations on their performances are also studied. Gizem Bölükbaşı Least cost network evaluation of grid and off-grid electrification systems Ayşe Selin Kocaman Abstract In 2007, only approximately %12 of Kenya’s 8 million households were connected to national grid and another %2-4 access electricity using alternatives sources which shows the lack of access. In this research, our aim is to find the optimum spatial electricity planning by implementing the Geomanns and Willimson’s algorithm for PCST (Prize Collecting Steiner Tree) which has a variety of applications for local access network design to Kenya case. Then, the obtained results will be compared with ones of a paper published in 2009 in which a modified version of Kruskal’s minimum spanning tree algorithm is used. Elfe Buluç Creating a Disaster Preparedness and Immediate Response System Bahar Yetiş Kara Abstract Disaster preparedness and immediate response are essential in terms of reducing the impact of disasters. Specifically during an earthquake, immediate actions play a vital role since a considerable amount of people who die during an earthquake could have saved if first aid had interfered immediately. An immediate response system is needed in local municipalities to protect the well-being of individuals more effectively. The paper will focus on optimizing the damage assessment and immediate decision making processes in local municipalities. Municipality of Çankaya will be used as pilot in order to implement the disaster response system. Mustafa Büyükkara Statistical Methods to Break Down Sentences into Sub-sentences Savaş Dayanık Abstract The research is in the area of natural language processing which uses statistics, machine learning and linguistics to derive meanings from human languages. We study the problem of identifying meaningful sub sentences from compound sentences. The outcome of this problem has potential applications in several natural language processing areas such as topic modelling, sentiment analysis. In this research, our aim is to find an unsupervised methodology for solving the problem. Nazlı Esen The Demand- Selective Location Routing Problem: the School Districting Application Bahar Yetiş Kara Abstract The main concept that we focus on this project is school districting application in Turkey. This application includes different types of models WTS, WTRS, DWTRS, CumWTRS and D-CumWTRS. The suitable algorithms will be generated and used to solve current models separately. The Vehicle Routing Problem concepts will be used to solve the problems. We will solve the problem with real data for specific area which is determined before. The result of this application directly depends on the problem parameters. Therefore, we will generate a JAVA code which will be help to solve problem for different problem parameters. Nur Kaynar Item Sorting Improvements in Warehouses Bahar Yetiş Kara & Özlem Çavuş Abstract The purpose of this research is to increase the throughput rate of the item sorting process in the warehouse owned by Ekol Logistics. The first phase of the project involves building a simulation model of the semi-automated sorting system in the warehouse by using the Arena Software. In the following phases, an improved order batching strategy, in terms of sorter efficiency and system time of sorting process, will be suggested through building a mathematical model. Yakup Koca Bundle Formation and Pricing with Customer-Level Retail Transaction Data Alp Akçay Abstract Optimal bundle formation and pricing by retailers requires knowing the joint distribution function of the reservation prices of customers, that is, how much a customer is willing to pay for each product. In practice, this information is unknown, and the uncertainty around the reservation-price distribution makes the optimal bundle composition and price questionable when this uncertainty is not accounted for in the optimization model. Assuming that the retailer has access to the price and purchase information at each customer transaction, we propose a joint estimation-optimization approach that accounts the uncertainty in the joint reservation-price distribution while making product bundling decisions. Barış Kocaman Alternative Formulations for Optimization Problems of Wireless Mesh Networks Kağan Gökbayrak Abstract A wireless mesh network (WMN) is a communications network made up of radio nodes organized in a mesh topology. WMNs provide cost-effective solutions for the communication in a certain geographic area as only a small number of radio nodes, called gateways, require wired connections. Traffic from clients is forwarded to gateway nodes in a multihop fashion to connect to the Internet. An inherent problem of WMNs is that simultaneous transmissions on the same frequency channel may interfere with each other preventing successful communication. Hence, interfering transmissions have to be scheduled at different times and on different channels. In this study, we jointly consider gateway placement, routing, and transmission slot assignment problems for WMNs. We present alternative formulations for our optimization problem and compare them in terms of the problem size, solution time and solution quality. Akın Öğrük Uncertainty Quantification in Milling Force Modeling Yiğit Karpat & Alp Akçay Abstract Mechanistic force coefficients in milling processes are typically determined by performing a linear regression to the mean force values with feed per tooth values as the independent variables. The linear regression approach, however, yields only a point estimate of each force coefficient and may require a large number of data to obtain a certain confidence. In this work, we use a Markov chain Monte Carlo method to update the prior information around the force coefficients as the new observations from physical experiments or process simulation accumulate. This allows us to characterize the uncertainty in the force coefficients. We also extend our analysis to milling processes in micro scale. Anıl Ömer Sarıtaç Order Batching in EKOL Logistics Warehouse Bahar Yetiş Kara & Özlem Çavuş Abstract This project involves discovering effective methods for improving the order batching process of the warehouse in Istanbul owned by EKOL Logistics. In general, this problem is critical for warehouses since order batching is often the most costly process that affects the whole system in warehouses. The goal is to minimize the total duration of processes at the warehouse that are in relation with the order batching process. While realizing this goal, mathematical programming will be the primary means. Munisse Kübra Şahin Branch and Cut Algorithms for the Split Delivery Vehicle Routing Problem Hande Yaman & Gizem Özbaygın Abstract This research investigates the implementation of branch and cut algorithms for the split delivery vehicle routing problem (SDVRP). In SDVRP, contrary to the classical vehicle routing problems, the demand of each customer can be fulfilled by several vehicles. Hence SDVRP is a relaxation of the capacitated vehicle routing problem. To solve SDVRP, there exists different approaches based on relaxed formulations which provide better bounds to the optimum. In this study, we analyze the solution approaches for SDVRP and try to improve the existing valid inequalities and branch and cut algorithms. Alper Şener A Lagrangian Relaxation Heuristic for the Load Balanced Facility Location Problem Hande Yaman Abstract In this study, we propose a Lagrangian relaxation based heuristic for the Load Balanced Facility Location Problem. In this problem, there are both lower and upper bounds on the demand allocated to a facility. We give an integer programming model and apply Lagrangian relaxation. The relaxed problems are two sided knapsack problems. We solve the relaxed problems using dynamic programming and develop a heuristic based on the relaxation. Sinan Türkmen Post-Disaster Medical Service Planning: Case For Istanbul Bahar Yetiş Kara Abstract The earthquake which occurred on August 17th disaster management. In the result of this experience, it is obvious that Turkish Armed Forces will have an important role in the new disaster model. There are many plans which deal with the chaos, which is going to appear after the possible “Big Istanbul Earthquake”. However, there is no plan about what will be the procedures if the hospital capacity becomes insufficient, how to locate the patients in the hospitals, which hospitals should be central hospitals in the result of such a big earthquake. This paper aims to save the amount of people as much as possible by using “the Turkish Air Force”s capabilities efficiently, for a situation which is possible to happen.

2013 Fall

 Student Name Project Title Advisor(s) Mustafa Kuş A Risk-Averse Dynamic Portfolio Optimization Problem Ozlem Cavus Abstract Ruszczynski (2010) used dynamic measures of risk to formulate risk-aversion in discrete-time Markov Decision Processes with finite and infinite horizon. In this study, we use this approach to model risk-aversion in a dynamic portfolio optimization problem. The problem we consider aims to find an optimal policy that maximizes a Markov dynamic risk measure of the total return of a portfolio over a finite time horizon and uses conditional mean-semideviation as one step conditional risk measures. We then replace the objective function with expected utility (see eg. Howard and Matheson (1972)) and compare the results of both risk-averse models. We use simulation to obtain the empirical distribution of the total return for both cases. Ali Erdem Banak The Robust Hub Location Problem Under Hose Demand Uncertainty Hande Yaman Abstract Hub location is a relatively new subject in location problems literature and there are few papers dealing with uncertainty in hub location problems. In this study, we use a polyhedral uncertainty description of demands. We provide a linear MIP model for the hub location problem under polyhedral demand uncertainty. We use the hose model and report the results of computational experiments. Vahid Emre Koksal Warehouse Operating Policies in the Context of Carbon Emission Cap Ulkü Gurler Abstract Taking carbon emission levels into consideration is essential for businesses, as being environmentally conscious plays a vital role for overcoming global warming problem. In this study, we are looking into the effects of the implementation of a cap and trade policy into a warehouse dispatching system. We mainly try to analyze the trade-off between carbon emission levels, and customer satisfaction depending on the delay of the goods. As we see a growth in the number of environmentally conscious customers, who demand producers to decrease their carbon emissions, we also need to understand how implementing carbon emission policies would affect the customer end. Thus, studying this relationship would help businesses greatly to understand the interaction between newly trending factors with the traditional system. The aim of this research is minimizing the cost in such a system that functions under a cap and trade policy, and other cost variables. Buse Tali A Periodic Review Inventory System with Time-Dependent Partial Ulku Gurler Abstract In this study, the impact of time dependent partial backordering is investigated under a periodic review (s, S) policy. It is assumed that the demands arrive as a homogeneous Poisson process when as long as on hand stock is positive. However, during a stockout period, the unsatisfied demand is partially backlogged with a decreasing probability that depends on the remaining replenishment time. This arrival pattern leads to a non-homogeneous Poisson process. Under these assumptions, the expected cost rate function is derived by incorporating holding, backlogging and lost sales costs. The impact of various backlogging probability functions will be investigated and numerical examples will be given to illustrate the model. Sinem Savaser Single Period Newsvendor Problem with Time Dependent Partial Backordering Policy for an Inventory System Ulku Gurler Abstract In this study, a time dependent partial backordering policy for an inventory system is considered under a newsvendor setting with a single period of length T. The starting inventory is denoted as Q. Demand arrivals are assumed to follow a Poisson process as long as on hand stock is positive. The demand during a stock out period is backlogged with a certain probability depending on the remaining replenishment time. Several functions regarding the impact of the remaining replenishment time on backlogging will be considered. Unlike the related works in the literature, we also consider time depending backlogging cost. Expected cost function will be derived. The aim is to find the optimal values of (Q,T) that minimizes the expected cost. Numerical examples will be provided. Kaan Caner Three in One: Critical Path, Time-Cost Trade Off and Resource Allocation carried out simultaneously by a Linear Programming Model for Project Scheduling Osman Oguz Abstract To solve scheduling projects, there exists three basic models. CPM/PERT for scheduling activities of a project based on predefined activity durations and precedence requirements, ignoring resource availability and allocation, and costs. Time-Cost Trade Off analysis using linear programming, which ignores resource requirements. Lastly, Integer Programming models to allocate scarce resources to activities, which minimizes duration and ignores costs. It is necessary to use all three models recursively to obtain a practical and economic schedule. The new approach proposed in this study consists of a single model that provides a solution satisfying all three purposes.

2012 Fall

 Student Name Project Title Advisor(s) Ismail Ozan Sert Carbon Sink Maximization in Budget Constrainted Afforestation Projects Nesim K. Erkip Abstract Increasing levels of greenhouse gases in the atmosphere and its implications have become an imminent concern for a plethora of parties. As a member of the developing world, Turkey is no different. Whilst environmentalists strive to maintain the ecologies intact, corporations are searching for alternative methods to ease the restrictions on their carbon footprints. Although afforestation projects are being invested in currently, they do not properly take into account the carbon aspect. This study endeavors to provide a location selection model optimizing the carbon sequestering capacities of newly planted forests under a limited budget. Natural parameters such as rainfall, soil type, compatible tree species and artificial parameters like forestfire precautions, maintenance costs and frequency are considered within the scope of the project. Utku Serhatlı Integration of Carbon Emission in Supply Chain Models Hande Yaman Abstract Global warming due to greenhouse gas(GHG) emission is one of the most controversial topics of 21st century. Concerns related to carbon emission are gaining attention at ever-increasing rate and becoming the subject of lots of heated debates. Many countries already committed to decrease their carbon emissions which ultimately force big retailer companies to reconsider their supply chain models. There is a large literature on cost minimization of supply chains which includes inventory, backorder, transportation of goods(from both customers’ and retailers’ perspective) and location. However, an emerging concept, carbon emission, is rarely included in the papers which we believe, has a huge potential to open new areas of investigation. Our aim is to develop a new model in order to integrate a carbon emission as a new component in our decision making process. Governmental decisions or company’s voluntary commitments to reduce carbon emissions plays crucial role in our analysis. The model under certain market regulations such as carbon cap, carbon tax and cap-and-trade will be examined. The regulation is an important factor to decide how to integrate carbon emission in our system i.e. either as objective or constraint. At this point we expect that transportation mode, number of stores, location of stores and stock policy are likely to play crucial role on how to structure the optimization problem. Nazlı Sönmez MARKETING IMPACT OF CARBON EMISSION AND PRICING Ülkü Gürler Abstract In this research, we consider the newsboy problem with carbon and price sensitive customers and multiple inputs. We assume that the production quantity has multiple inputs with different costs and carbon emissions. It is also assumed that the customers are environment conscious. The potential link functions that connect the random demand to carbon footprints will be investigated and the optimal production and input mixture will be determined under several demand functions. Özge Yapar Road Capacity Calculation and Its Effects on Post-Disaster Evacuation Routes and Shelter Locations Barbaros Tansel Abstract In the case of sudden environmental disasters, post-disaster evacuation is a critical issue because there is limited time to transport victims to safety, which is defined as shelters that have accommodation units, food, water and medical supply. In order to evacuate victims quickly, it is reasonable to utilize the shortest path to an available shelter. However, this approach may result in traffic congestion in some roads which yields slower speeds, longer travel times and vehicular queuing. Therefore, traffic congestion should be taken into account during the determination of evacuation routes. Indeed, since evacuation routes and location of shelters are interrelated, traffic congestion is also a relevant factor in selection of shelter locations. The aim of this study is to propose a maximum road capacity calculation that prevents traffic congestion and incorporate road capacities into evacuation route and shelter location optimization.

2011 Sping

 Student Name Project Title Advisor(s) CANSU KAPAN SAHIN Quantification of Input Model Uncertainty in Inventory Simulations Canan Güneş Çorlu MURAT TİNİÇ Comparison of Flexible Distributions in Modeling Financial Returns Canan Güneş Çorlu

2011 Fall

 Student Name Project Title Advisor(s) BURCU TEKIN Quantifying the Parameter Uncertainty in an Inventory Simulation Canan Güneş Çorlu MUSTAFA KARATAS Finding an Appropriate Method in Forecasting Intermittent Demands Alper Sen HUSEYIN GURKAN Dynamic Bidding Strategies for Multiple Keywords in Search-based Advertising Savaş Dayanık GALIP ORAN OKAN Parameter Estimation for the Generalized Lambda Distribution Canan Güneş Çorlu

2010 Spring

 Student Name Project Title Advisor(s) AMINE GIZEM OZBAYGIN A study of traffic loads and congestion in post disaster evacuation of a region via a road network Barbaros Tansel BASAK YAZAR A Different Location Routing Problem (LRP): School District Bahar Yetiş Kara MELTEM PEKER Spatial Analysis of Hub Location Bahar Yetiş Kara

2010 Fall

 Student Name Project Title Advisor(s) ONUR MUTLU Mixed Model Assembly Line Balancing Kağan Gökbayrak Mehmet R. Taner ALI INAY Linear Programming Study of the Network Flow Based Model for the P-median Problem Barbaros Tansel ALI IRFAN MAHMUTOGULLARI Inventory Management under Supply Uncertainty Nesim K. Erkip OKAN DUKKANCI Bicriteria Scheduling with deterministic machine availability constraints on a single machine Mehmet R. Tanerk NUR TIMURLENK Solving Sudoku Puzzles: Formulations, Cuts, and Bounds Emre Alper Yıldırım GIZEM SAGOL Analysis of Change Point Detection Methods Savaş Dayanık AHMET COLAK Optimal Control of One-Dimensional Random Walk with Penalty for Late Detection Savaş Dayanık

2009 Spring

 Student Name Project Title Advisor(s) FEYZA GULIZ SAHINYAZAN Heuristic Approaches for Effective Management of the Mobile Blood Donation Units at Turkish Red Crescent Bahar Yetiş Kara Mehmet R. Taner ALI INAY A Computational Study of a New Formulation of the p-Median Problem Barbaros Tansel ASLIGUL SERASU DURAN Stochastic Inventory Control with Backorders and Lead Time Quotations Mehmet Murat Fadıloğlu ELIF KETEN Dynamic Programming Approach for Portfolio Optimization in Discrete Time Savaş Dayanık ALI YESILCIMEN A Computational Study of a New Formulation of Traveling Salesman Problem Barbaros Tansel ENES BILGIN An experimental study to explore properties of optimal forests in p-median problems Barbaros Tansel YIGITALP MEDIN A Computational Study for Determining Hub Locations and Number of Aircrafts in Turkish Airlines Cargo Deliveries to Europe Bahar Yetiş Kara SERTALP BILAL CAY An Online Tool for Production Line Performance Evaluation Mehmet Murat Fadıloğlu

2009 Fall

 Student Name Project Title Advisor(s) SERCAN ALTUN Testbed for Joint Channel Allocation, Routing and Link Scheduling Research in Wireless Mesh Networks Kağan Gökbayrak CAGIN ARARAT A Lot Size – Reorder Level System for the Inventory Control of Decreasing Demand Items Nesim K. Erkip ENES BILGIN A computational study of a new formulation of p-median problem based on spanning forests Barbaros Tansel SERTALP BILAL CAY An Internet Decision Support System for Analysis of Production Lines Mehmet Murat Fadıloğlu PELIN DIREN Heuristics for Control of Wireless Mesh Networks Emre Alper Yıldırım Kağan Gökbayrak ASLIGUL SERASU DURAN Stochastic Inventory Control with Backorders and Lead Time Quotations Mehmet Murat Fadıloğlu EMRE KARA Optimal Stopping for Perpetual American Options in Discrete Time Mustafa Ç. Pınar FEYZA GULIZ SAHINYAZAN Optimization of Blood Donation via Mobile Units at Turkish Red Crescent Bahar Yetiş Kara Mehmet R. Taner OLCAY SARMAZ Exact Methods for Joint Channel Assignment, Routing and Link Scheduling in Multi-radio Wireless Mesh Networks Kağan Gökbayrak Emre Alper Yıldırım SIBEL SOZUER Joint Pricing and Order Quantity Decisions under a Customer Reservation Price Distribution Ülkü Gürler MUGE TEKIN Quickest detection of a sudden shift in the mean of a Gaussian distribution Savaş Dayanık

2008 Spring

 Student Name Project Title Advisor(s) ZIYAATTIN HUSREV AKSUT Intermodal Multicommodity Routing Problem with Scheduled Services and Piecewise Linear Transportation Costs Hande Yaman ALI DENIZ GULACTI Hub Location Problem for Intermodal and Incomplete Logistics Networks — CAGATAY KARAN Dynamic Policies for Stochastic Economic Lot Scheduling Problems Mehmet Murat Fadıloğlu GORKEM YURTLU Maximizing Blood Collection via a new Mobile Collection Network at Turkish Red Crescent Bahar Yetiş Kara Mehmet R. Taner

2008 Fall

 Student Name Project Title Advisor(s) TARDU SELIM SEPIN Optimal Replication of Options with Transaction Costs and Trading Restrictions Mustafa Ç. Pınar ZEYNEP YETIS Retailer Problem in Pricing and Ordering Decisions for Substitutable Products Nesim K. Erkip GORKEM YURTLU The Logistic Planning and Scheduling for Mobile Units of Turkish Red Crescent Bahar Yetiş Kara Mehmet R. Taner GOKHAN MEMISOGLU Producer’s Problem in a Food Supply Chain: Optimal Product Mix and Contract Type Nesim K. Erkip PINAR AYTEN GUVENC Promotion Planning with Inventory Considerations Alper Sen CAGATAY KARAN Dynamic Policies for Stochastic Economic Lot Scheduling Problems Mehmet Murat Fadıloğlu KONUR BAYRAMOGLU Manufacturer’s Mixed Pallet Design Problem Hande Yaman Alper Sen TUGCE TALI A Branch-and-Cut Algorithm for the Heterogeneous Vehicle Routing Problem Hande Yaman Mehmet R. Taner FILIZ SAYIN Inventory Control Under Dynamic Price and Lead-Time Quotations Mehmet Murat Fadıloğlu Emre Alper Yıldırım MEHMET OZCAN A Storage Layout Problem in a Spare Parts Warehouse with Multi-Command Picking Tours Barbaros Tansel CEYDA ELBASIOGLU Forecasting and Order Quantity Determination with Two Products Ülkü Gürler MERVE NAZLI ERALP Locating Detection Stations for Illegal Border Crossing Barbaros Tansel MEHMET DIYAR YATKIN A Receding Horizon Controller for Flow Shop Systems Kağan Gökbayrak