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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 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 discrete-continuous optimization approach for the design and operation of synchromodal transportation networks(Elsevier, 2019) Reşat, Hamdi Giray; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a multi-objective mixed-integer programming problem for integrating specific characteristics of synchromodal transportation. The problem includes different objective functions including total transportation cost, travel time and CO2 emissions while optimizing the proposed network structure. Traffic congestion, time-dependent vehicle speeds and vehicle filling ratios are considered and computational results for different illustrative cases are presented with real data from the Marmara Region of Turkey. The defined non-linear model is converted into linear form and solved by using a customized implementation of the e-constraint method. Then, the sensitivity analysis of proposed mathematical models with pre-processing constraints is summarized for decision makers.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 Open Access A hierarchical solution approach for a multicommodity distribution problem under a special cost structure(Elsevier, 2012) Koca, Esra; Department of Industrial Engineering; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; College of EngineeringMotivated by the spare parts distribution system of a major automotive manufacturer in Turkey, we consider a multicommodity distribution problem from a central depot to a number of geographically dispersed demand points. The distribution of the items is carried out by a set of identical vehicles. The demand of each demand point can be satisfied by several vehicles and a single vehicle is allowed to serve multiple demand points. For a given vehicle, the cost structure is dictated by the farthest demand point from the depot among all demand points served by that vehicle. The objective is to satisfy the demand of each demand point with the minimum total distribution cost. We present a novel integer linear programming formulation of the problem as a variant of the network design problem. The resulting optimization problem becomes computationally infeasible for real-life problems due to the large number of integer variables. In an attempt to circumvent this disadvantage of using the direct formulation especially for larger problems, we propose a Hierarchical Approach that is aimed at solving the problem in two stages using partial demand aggregation followed by a disaggregation scheme. We study the properties of the solution returned by the Hierarchical Approach. We perform computational studies on a data set adapted from a major automotive manufacturer in Turkey. Our results reveal that the Hierarchical Approach significantly outperforms the direct formulation approach in terms of both the running time and the quality of the resulting solution especially on large instances.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 Metadata only A learning based algorithm for drone routing(Pergamon-Elsevier Science Ltd, 2022) N/A; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Ermağan, Umut; Yıldız, Barış; Salman, Fatma Sibel; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 258791; 178838We introduce a learning-based algorithm to solve the drone routing problem with recharging stops that arises in many applications such as precision agriculture, search and rescue, and military surveillance. The heuristic algorithm, namely Learn and Fly (L&F), learns from the features of high-quality solutions to optimize recharging visits, starting from a given Hamiltonian tour that ignores the recharging needs of the drone. We propose a novel integer program to formulate the problem and devise a column generation approach to obtain provably high-quality solutions that are used to train the learning algorithm. Results of our numerical experiments with four groups of instances show that the classification algorithms can effectively identify the features that determine the timing and location of the recharging visits, and L&F generates energy feasible routes in a few seconds with around 5% optimality gap on the average.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 nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain(Pergamon-Elsevier Science Ltd, 2016) Kaya, Onur; Ürek, Büşra; PhD Student; Graduate School of Sciences and Engineering; N/AWe analyze a network design problem for a closed-loop supply chain that integrates the collection of the used products with the distribution of the new products. We present a mixed integer nonlinear facility location-inventory-pricing model to decide on the optimal locations of the facilities, inventory amounts, prices for new products and incentive values for the collection of right amount of used products in order to maximize the total supply chain profit. We develop heuristics for the solution of this model and analyze the effectiveness of these heuristics and the effects of the parameters on this system through numerical experiments.Publication Open Access A model-based heuristic to the min max K-arc routing for connectivity problem(Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2014) Akbari, Vahid; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We consider the post-disaster road clearing problem with the goal of restoring network connectivity in shortest time. Given a set of blocked edges in the road network, teams positioned at depot nodes are dispatched to open a subset of them that reconnects the network. After a team finishes working on an edge, others can traverse it. The problem is to find coordinated routes for the teams. We generate a feasible solution using a constructive heuristic algorithm after solving a relaxed mixed integer program. In almost 70 percent of the instances generated both randomly and from Istanbul data, the relaxation solution turned out to be feasible, i.e. optimal for the original problem.