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Publication Metadata only A bicriteria approach to the two-machine flow shop scheduling problem(Elsevier Science Bv, 1999) N/A; Department of Business Administration; Department of Business Administration; Sayın, Serpil; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 6755; 38819In this paper we address the problem of minimizing makespan and sum of completion times simultaneously in a two-machine flow shop environment. We formulate the problem as a bicriteria scheduling problem, and develop a branch-and-bound procedure that iteratively solves restricted single objective scheduling problems until the set of efficient solutions is completely enumerated. We report computational results, and explore certain properties of the set of efficient solutions. We then discuss their implications for the Decision Maker.Publication Metadata only A decision support framework for evaluating revenue performance in sequential purchase contexts(Elsevier Science Bv, 2017) Öztürk, O. Cem; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819This paper studies the product ordering problem in sequential purchase contexts where sellers aim to maximize their revenue faced with budget constrained buyers. We propose a multi-layered decision support framework that combines empirical data with simulation, optimization, and econometric methods to address this problem. Our framework allows sellers to: (i) compare revenue performances of limited information sequencing strategies, (ii) quantify benchmark revenue levels that can be achieved via the optimal sequence based on detailed buyer information, (iii) determine the costs of limited information and strategic buyers to the seller, and (iv) identify the moderators of sequencing strategy performance. We illustrate our framework through two applications in a business-to-business used-car auction setting. Contrary to previous studies reporting practitioners’ tendency to sequence items from the lowest value to the highest, our results suggest that the best-performing limited information sequencing strategy depends on buyers’ bidding behavior. We also find that the revenue difference between the optimal sequence and a limited information sequencing strategy can be substantial. Our results show that a significant portion of this revenue difference is associated with the seller’s limited information on buyers’ budgets and product valuations. Our applications also provide various sensitivity analyses and develop new propositions on the moderators of the relationship between the seller’s revenue and sequencing strategies.Publication Metadata only A decomposition model for continuous materials flow production systems(Taylor & Francis, 1997) Yeralan, Sencer; Department of Business Administration; N/A; Tan, Barış; Faculty Member; N/A; Department of Business Administration; College of Administrative Sciences and Economics; N/A; 28600; N/AThis study presents a general and flexible decomposition method for continuous materials flow production systems. The decomposition method uses the station model developed in the first part of this study (Yeralan and Tan 1997). The decomposition method is an iterative method. At each iteration the input and output processes of the station model are matched to the most recent solutions of the adjacent stations. The procedure terminates when the solutions converge and the conservation of materials flow is satisfied. The decomposition method does not alter the station parameters such as the breakdown, repair, and service rates. This method can be used to analyse a wide variety of production systems built from heterogeneous stations. The properties of the decomposition method are studied for the series arrangement of workstations. The convergence and uniqueness of the decomposition method are discussed. The method is compared to other approximation methods. The complexity of the decomposition method is empirically investigated and is shown to be in the order of N-2 where N is the number of stations in the line, irrespective of the buffer capacities.Publication Metadata only A fuzzy decomposition method for multistation production systems subject to blocking(Elsevier Science Bv, 1996) Yeralan, Sencer; Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600This study presents a new methodology to adjust the value of the proportionality constant (step length parameter) used in the general decomposition method for multistation heterogeneous production systems proposed in an earlier study for specially unbalanced production systems by using fuzzy logic control. The decomposition method is based on successive approximations. Namely, input rate to each subsystem is adjusted proportional to the difference in production rates of adjacent stations. This process continues until all the subsystems have the same production rate, Fuzzy logic control uses basic observations described in linguistic variables of how production rate changes as a function of input rate, Consequently, the proportionality constant in the successive approximation method is adjusted. These observations are not model specific, Thus, the fuzzy decomposition method can be applied to a wide variety of production systems. The same methodology can also be used in other applications where adjusting the step length parameter to attain the highest convergence rate is not trivial. For example, step length parameter used in subgradient optimization and other search methodologies can also be adjusted by using the fuzzy logic control methodology presented in this study. Numerical experience shows that this method yields a substantial improvement in the convergence rate of the decomposition method for highly unbalanced production system.Publication Metadata only A lab-scale manufacturing system environment to investigate data-driven production control approaches(Elsevier Sci Ltd, 2021) N/A; N/A; Department of Business Administration; Khayyati, Siamak; Tan, Barış; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600Controlling production and release of material into a manufacturing system effectively can lower work-inprogress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a datadriven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the datadriven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.Publication Open Access A machine learning approach for implementing data-driven production control policies(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.Publication Metadata only A min-max-sum resource allocation problem and its applications(Informs, 2001) Kouvelis, P.; Yu, G.; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819In this paper we consider a class of discrete resource-allocation problems with a min-max-sum objective function. We first provide several examples of practical applications of this problem. We then develop a branch-and-bound procedure for solving the general case of this computationally intractable problem. The proposed solution procedure employs a surrogate relaxation technique to obtain lower and upper bounds on the optimal objective function value of the problem. To obtain the multipliers of the surrogate relaxation, two alternative approaches are discussed. We also discuss a simple approximation algorithm with a tight bound. Our computational results support the effectiveness of the branch-and-bound procedure for fairly large-size problems.Publication Metadata only A min-sum-max resource allocation problem(Kluwer Academic Publ, 2000) Kouvelis, P.; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819In this paper we describe a class of resource allocation problems with a min–sum–max objective function. We first discuss practical applications of the problem. We then present a result on the computational complexity of the problem. We propose an implicit enumeration procedure for solving the general case of the problem, and report on our computational experience with the solution procedure.Publication Metadata only A mixed integer programming formulation for the l-maximin problem(Stockton Press, 2000) N/A; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755In this paper, I present a mixed integer programming (MIP) formulation for the 1-maximin problem with rectilinear distance. The problem mainly appears in facility location while trying to locate an undesirable facility. The rectilinear distance is quite Commonly used in the location literature. Our numerical experiments show that one can solve reasonably large location problems using a standard MIP solver. We also provide a linear programming formulation that helps find an upper bound on the objective function value of the 1-maximin problem with any norm when extreme points of the feasible region are known. We discuss various extension alternatives for the MIP formulation.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.