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Publication Metadata only A bi-objective model for design and analysis of sustainable intermodal transportation systems: A case study of Turkey(Taylor & Francis Ltd, 2019) Reşat, Hamdi Giray; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a mixed-integer linear optimisation model to analyse the intermodal transportation systems in the Turkish transportation industry. The solution approach includes mathematical modelling, data analysis from real-life cases and solving the resulting mathematical programming problem to minimise total transportation cost and carbon dioxide emissions by using two different exact solution methods in order to find the optimal solutions. The novel approach of this paper generates Pareto solutions quickly and allows the decision makers to identify sustainable solutions by using a newly developed solution methodology for bi-objective mixed-integer linear problems in real-life cases.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 binarization strategy for modelling mixed data in multigroup classification(Institute of Electrical and Electronics Engineers (IEEE), 2013) Masmoudi, Youssef; Chabchoub, Habib; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes with a wide range to use a small number of binary attributes. Once the data set is pre-processed, we use the Support Vector Machine (LibSVM) for classification. The proposed method was tested on datasets from the literature. We demonstrate the improved accuracy and efficiency of presented binarization strategy for modelling mixed and complex data in comparison to the classification of the original dataset, nominal dataset and binary dataset.Publication Metadata only A blood bank network design problem with ıntegrated facility location, ınventory and routing decisions(Springer, 2020) Kaya, Onur; N/A; Özkök, Doğuş; Master Student; Graduate School of Sciences and Engineering; N/AWe aim to design an effective supply chain network for a blood distribution system to satisfy the needs of hospitals in a certain region. In the analyzed current system, each hospital keeps a certain level of inventory, received at certain time periods by the shipments from a main blood bank. We propose an alternative system, in which some of the hospitals are selected as local blood banks (LBB) and all other hospitals will be assigned to an LBB. More frequent shipments will be made from LBBs to these hospitals, leading to lower inventory levels to be kept at each hospital. The inventories kept separately at the hospitals in the current system will be pooled at the selected LBBs in the proposed system. We develop a mixed integer nonlinear programming (MINLP) model to determine the optimal selection of LBBs, assignment of hospitals to LBBs, optimal inventory levels at each LBB and routing decisions among the facilities in order to minimize total system costs. We also propose a piecewise linear approximation method and a simulated annealing heuristic approach to find the solution of this problem. The proposed model and the solution techniques are applied on a real life case study for the blood distribution network in Istanbul. It is observed that significant improvements can be obtained by the proposed system when compared to the current design. Performances of the solution methods are also compared and a sensitivity analysis related to system parameters is presented via detailed numerical experiments.Publication Metadata only A constant-factor approximation algorithm for multi-vehicle collection for processing problem(Springer Heidelberg, 2013) Gel, Esma S.; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Yücel, Eda; Salman, Fatma Sibel; Örmeci, Lerzan; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 235501; 178838; 32863We define the multiple-vehicle collection for processing problem (mCfPP) as a vehicle routing and scheduling problem in which items that accumulate at customer sites over time should be transferred by a series of tours to a processing facility. We show that this problem with the makespan objective (mCfPP()) is NP-hard using an approximation preserving reduction from a two-stage, hybrid flowshop scheduling problem. We develop a polynomial-time, constant-factor approximation algorithm to solve mCfPP(). The problem with a single site is analyzed as a special case with two purposes. First, we identify the minimum number of vehicles required to achieve a lower bound on the makespan, and second, we characterize the optimal makespan when a single vehicle is utilized.Publication Metadata only A coordinated production and shipment model in a supply chain(Elsevier Science Bv, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a deterministic inventory system. In this supply chain, the customers are willing to wait at the expense of a waiting cost. Accordingly, the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time. The supplier produces the items, holds the inventory and ships the products to the retailer to satisfy the external demand. We investigate both a coordinated production/transportation model and a decentralized model. In the decentralized model, the retailer manages his own system and sends orders to the supplier, while the supplier determines her own production process and the amount to produce in an inventory replenishment cycle according to the order quantity of the retailer. However, in the coordinated model, the supplier makes all the decisions, so that she determines the length of the replenishment and transportation cycles as well as the shipment quantities to the retailer. We determine the structure of the optimal replenishment and transportation cycles hi both coordinated and decentralized models and the corresponding costs. Our computational results compare the optimal costs under the coordinated and decentralized models. We also numerically investigate the effects of several parameters on the optimal solutions.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 dynamic inventory rationing problem with uncertain demand and production rates(Springer, 2015) Turgay, Zeynep; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Fikri; Örmeci, Lerzan; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 3579; 32863We investigate the structural properties of a finite horizon, discrete time single product inventory rationing problem, where we allow random replenishment (production) opportunities. In contrast to the standard models of dynamic capacity control in revenue management or production/inventory systems, we assume that the demand/production rates are not known with certainty but lie in some interval. To address this uncertainty, we formulate a robust stochastic dynamic program and show how the structural properties of the optimal policy propagate to the robust counterpart of the problem. Further, we explore how the optimal policy changes with respect to the uncertainty set. We also show that our results can be extended to certain alternative robust formulations.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.