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Publication Metadata only A Markov modulated Poisson model for software reliability(Elsevier, 2013) Landon, Joshua; Soyer, Refik; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.Publication Metadata only A matheuristic for the generalized order acceptance and scheduling problem(Elsevier, 2022) N/A; Department of Industrial Engineering; Tarhan, İstenç; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033In make-to-order production systems, manufacturer can have limited capacity and due to the order de-livery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem with release times and sequence dependent setup times that determines which orders to accept and how to schedule them simultaneously to maximize the revenue (GOAS). The aim of this study is to develop an effective and efficient solution methodology for the GOAS problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model, and a matheuristic algorithm that consists of a time-bucket based mixed integer linear programming model, a variable neighborhood search algorithm and a tabu search algorithm. Computational results show that the proposed matheuristic outperforms both the proposed exact models and previous state-of-the-art al-gorithms developed for the GOAS problem. The boundary of optimally solved instance size is pushed further and near optimal solutions are obtained in reasonable time for instances falling beyond this boundary.Publication Metadata only A mixed-integer programming approach to multi-class data classification problem(Elsevier Science Bv, 2006) Department of Industrial Engineering; Department of Industrial Engineering; Yüksektepe, Fadime Üney; Türkay, Metin; Researcher; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 108243; 24956This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on the well-known IRIS data set. The computational results on the illustrative example and the IRIS data set show that the proposed method is accurate and efficient on multi-class data classification problems.Publication Metadata only A mixed-integer programming approach to the clustering problem with an application in customer segmentation(Elsevier, 2006) Sağlam, Burcu; Department of Industrial Engineering; Department of Business Administration; Department of Industrial Engineering; Salman, Fatma Sibel; Sayın, Serpil; Türkay, Metin; Faculty Member; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Engineering; College of Administrative Sciences and Economics; College of Engineering; 178838; 6755; 24956This paper presents a mathematical programming based clustering approach that is applied to a digital platform company's customer segmentation problem involving demographic and transactional attributes related to the customers. The clustering problem is formulated as a mixed-integer programming problem with the objective of minimizing the maximum cluster diameter among all clusters. In order to overcome issues related to computational complexity of the problem, we developed a heuristic approach that improves computational times dramatically without compromising from optimality in most of the cases that we tested. The performance of this approach is tested on a real problem. The analysis of our results indicates that our approach is computationally efficient and creates meaningful segmentation of data.Publication Metadata only A modeling framework for control of preventive services(Informs, 2016) Kunduzcu, Derya; Department of Industrial Engineering; Department of Business Administration; Örmeci, Lerzan; Güneş, Evrim Didem; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Business Administration; College of Engineering; College of Administrative Sciences and Economics; 32863; 51391We present a modeling framework for facilities that provide both screening (preventive) and diagnostic (repair) services. The facility operates in a random environment that represents the condition of the population that needs screening and diagnostic services, such as the disease prevalence level. We model the environment as a partially endogenous process: the population's health can be improved by providing screening services, which reduces future demand for diagnostic services. We use event-based dynamic programming to build a framework for modeling different kinds of these facilities. This framework contains a number of service priority policies that are concerned with prioritizing screening versus diagnostic services. The main trade-off is between serving urgent diagnostic needs and providing screening services that may decrease future diagnostic needs. Under certain conditions, this trade-off reverses the famous c,u, rule; i.e., the patients with lower waiting cost are given priority over the others. We define appropriate event operators and specify the properties preserved by these operators. These characterize the structure of optimal policies for all models that can be built within this framework. A numerical study on colonoscopy services illustrates how the framework can be used to gain insights on developing good screening policies.Publication Metadata only A newsvendor problem with markup pricing in the presence of within-period price fluctuations(Elsevier, 2022) Canyakmaz, Caner; Department of Industrial Engineering; Department of Industrial Engineering; Özekici, Süleyman; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 32631; 3579We consider a single-item single-period joint inventory management and pricing problem of a retailer selling an item that has selling price uncertainties. Unlike most of the literature on the newsvendor problem, we assume that price-dependent demand arrives randomly according to a stochastic arrival process whose rate depends on the fluctuating market input price process. The retailer's problem is to choose the order quantity and a proportional price markup over the input price to maximize the expected profit. This setting is mostly encountered by retailers that trade in different currencies or have to purchase and convert commodities for seasonal sales. For this setting, we characterize both the optimal inventory and markup levels. We present monotonicity properties of the expected profit function with respect to each decision variable. We also show that more volatile input price processes lead to lower expected profits.Publication Metadata only A one direction search method to find the exact nondominated frontier of biobjective mixed-binary linear programming problems(Elsevier Science Bv, 2018) Fattahi, Ali; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956The nondominated frontier (NDF) of a biobjective optimization problem is defined as the set of feasible points in the objective function space that cannot be improved in one objective function value without worsening the other. For a biobjective mixed-binary linear programming problem (BOMBLP), the NDF consists of some combination of isolated points and open, closed, or half-open/half-closed line segments. Some algorithms have been proposed in the literature to find an approximate or exact representation of the NDF. We present a one direction search (ODS) method to find the exact NDF of BOMBLPs. We provide a theoretical analysis of the ODS method and show that it generates the exact NDF. We also conduct a comprehensive experimental study on a set of benchmark problems and show the solution quality and computational efficacy of our algorithm.Publication Open Access Agricultural planning of annual plants under demand, maturation, harvest, and yield risk(Elsevier, 2012) Department of Industrial Engineering; Tan, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Administrative Sciences and Economics; N/A; 28600In this study we present a planning methodology for a firm whose objective is to match the random supply of annual premium fruits and vegetables from a number of contracted farms and the random demand from the retailers during the planning period. The supply uncertainty is due to the uncertainty of the maturation time, harvest time, and yield. The demand uncertainty is the uncertainty of weekly demand from the retailers. We provide a planning methodology to determine the farm areas and the seeding times for annual plants that survive for only one growing season in such a way that the expected total profit is maximized. Both the single period and the multi period cases are analyzed depending on the type of the plant. The performance of the solution methodology is evaluated by using numerical experiments. These experiments show that the proposed methodology matches random supply and random demand in a very effective way and improves the expected profit substantially compared to the planning approaches where the uncertainties are not taken into consideration. (c) 2012 Elsevier B.V. All rights reserved.Publication Metadata only An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem(Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/AWe study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.Publication Metadata only An alternative polynomial-sized formulation and an optimization based heuristic for the reviewer assignment problem(Elsevier, 2019) Yeşilçimen, Ali; Department of Industrial Engineering; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; College of Engineering; 28415Peer review systems are based on evaluating a scholarly work, referred to as a proposal, by experts in that field. In such a system, we consider the reviewer assignment problem, i.e., the problem of assigning proposals to reviewers under the assumption that each reviewer returns her preferences using ordinal rankings. Motivated by the problem defined in Cook et al. (Management Science, 51:655-661, 2005), we focus on reviewer assignments so as to maximize the total number of pairwise comparisons of proposals while ensuring a balanced coverage of distinct pairs of proposals. We propose an alternative mixed integer linear programming formulation for the reviewer assignment problem. In contrast to the optimization model proposed by Cook et al. (2005), the size of our formulation is polynomial in the input size. We present a semidefinite programming relaxation of our optimization model. Furthermore, we propose an optimization based heuristic approach, in which an optimal solution of the linear programming relaxation or the semidefinite programming relaxation of our optimization model is rounded in a straight-forward fashion, followed by a local improvement scheme based on pairwise exchanges of proposals. Our computational results illustrate the effectiveness of our optimization model and our heuristic approach.