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Publication Metadata only A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions(Elsevier, 2017) N/A; N/A; Department of Industrial Engineering; Kabatepe, Bora; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Electric vehicles (EV) are emerging as a mobility solution to reduce emissions in the transportation sector. The studies environmental impact analysis of EVs in the literature are based on the average energy mix or pre-defined generation scenarios and construct policy recommendations with a cost minimization objective. However, the environmental performance of EVs depends on the source of the marginal electricity provided to the grid and single objective models do not provide a thorough analysis on the economic and environmental impacts of EVs. In this paper, these gaps are addressed by a four step methodology that analyzes the effects of EVs under different charging and market penetration scenarios. The methodology includes a bi-criteria optimization model representing the electricity market operations. The results from a real-life case analysis show that EVs decrease costs and emissions significantly compared to conventional vehicles.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 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 multi-objective optimization approach for sustainable supply chains incorporating business strategy(IEEE, 2019) N/A; Department of Industrial Engineering; Bozgeyik, Esma Nur; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Sustainability is a necessity in the design and operation of supply chains. the triple bottom line (TBL) accounting of sustainability needs to incorporate economic, environmental and social pillars simultaneously in the decision making process. the business strategy can be developed to promote sustained growth, Also incorporating in the supply chain management issues as a business strategy rather than philanthropy. Deciding on the location of business facilities, supplier-manufacturer network, manufacturer-demand location network and the supplier- manufacturer relation strategy are among the important decisions in business strategy and supply chain management. However, there is a lack of theoretical work which analyzes the business strategy together with TBL concept of sustainability for the supply chain network design problem. in this paper, A methodological approach based on mathematical programming is proposed that conforms to the TBL accounting for supply chain network design problem from suppliers to customers embedded with business strategy and green energy usage option. a realistic case study is applied to the model. the results show that working with inclusive suppliers and using green energy are preferred with highest profit value.Publication Metadata only A tabu search algorithm for order acceptance and scheduling(Pergamon-Elsevier Science Ltd, 2012) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Cesaret, Bahriye; Oğuz, Ceyda; 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; 256439; 6033; 178838We consider a make-to-order production system, where limited production capacity and order delivery requirements necessitate selective acceptance of the orders. Since tardiness penalties cause loss of revenue, scheduling and order acceptance decisions must be taken jointly to maximize total revenue. We present a tabu search algorithm that solves the order acceptance and scheduling problem on a single machine with release dates and sequence dependent setup times. We analyze the performance of the tabu search algorithm on an extensive set of test instances with up to 100 orders and compare it with two heuristics from the literature. In the comparison, we report optimality gaps which are calculated with respect to bounds generated from a mixed integer programming formulation. The results show that the tabu search algorithm gives near optimal solutions that are significantly better compared to the solutions given by the two heuristics. Furthermore, the run time of the tabu search algorithm is very small, even for 100 orders. The success of the proposed heuristic largely depends on its capability to incorporate in its search acceptance and scheduling decisions simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.Publication Metadata only A variable neighborhood search for minimizing total weighted tardiness with sequence dependent setup times on a single machine(Pergamon-Elsevier Science Ltd, 2012) N/A; N/A; Department of Industrial Engineering; Kirlik, Gökhan; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033This paper deals with the single machine scheduling problem to minimize the total weighted tardiness in the presence of sequence dependent setup. Firstly, a mathematical model is given to describe the problem formally. Since the problem is NP-hard, a general variable neighborhood search (GVNS) heuristic is proposed to solve it. Initial solution for the GVNS algorithm is obtained by using a constructive heuristic that is widely used in the literature for the problem. The proposed algorithm is tested on 120 benchmark instances. The results show that 37 out of 120 best known solutions in the literature are improved while 64 instances are solved equally. Next, the GVNS algorithm is applied to single machine scheduling problem with sequence dependent setup times to minimize the total tardiness problem without changing any implementation issues and the parameters of the GVNS algorithm. For this problem, 64 test instances are solved varying from small to large sizes. Among these 64 instances, 35 instances are solved to the optimality, 16 instances' best-known results are improved, and 6 instances are solved equally compared to the best-known results. Hence, it can be concluded that the GVNS algorithm is an effective, efficient and a robust algorithm for minimizing tardiness on a single machine in the presence of setup times.Publication Open Access An empirical analysis of the main drivers affecting the buyer surplus in E-auctions(Taylor _ Francis, 2018) Department of Business Administration; Department of Industrial Engineering; Karabağ, Oktay; Tan, Barış; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; N/A; 28600We empirically examine the impacts of the product category, the auction format, the 2008 global financial crisis, the group purchasing, the contract type, the platform ownership, and the number of participating suppliers on the buyer surplus obtained from e-auctions. To this end, we collect a unique dataset from a purchasing organisation that offers e-auction solutions to its corporate customers. By using a standard Tobit model, we show that the product categories, the auction type, and the number of participating suppliers have significant effects on the decrease in the procurement prices with respect to the minimum of the initial submitted bids. It is observed that the 2008 global financial crisis led to an increase in the buyer surplus. We classify the product categories into three groups based on their impacts on the average of the decrease in the procurement prices. We show that the average decrease in procurement prices is higher for the group purchasing option than for the individual buying option. It is concluded that the types of contract between buyers and auctioneer and the platform ownership have no statistically significant effects on the average decrease in procurement prices.Publication Open Access An improved lower bound on the competitive ratio of deterministic online algorithms for the multi-agent k-Canadian Traveler Problem(Finding Press, 2022) Shiri, Davood; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We present an improved lower bound on the competitive ratio of deterministic online algorithms for the multi-agent k-Canadian Traveler Problem.Publication Metadata only An inventory model where customer demand is dependent on a stochastic price process(Elsevier Science Bv, 2019) 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 investigate the optimal inventory operations of a firm selling an item whose price is driven by an exogenous stochastic price process which consequently impacts customer arrivals between ordering cycles. This case is typical for retailers that operate in different currencies, or trade products consisting of commodities or components whose prices are subject to market fluctuations. We assume that there is a stochastic input price process for the inventory item which determines purchase and selling prices according to a general selling price function. Customers arrive according to a doubly-stochastic Poisson process that is modulated by stochastic input prices. We analyze optimal ordering decisions for both backorder and lost-sale cases. We show that under certain conditions, a price-dependent base stock policy is optimal. Our analysis is then extended to a price-modulated compound Poisson demand case, and the case with fixed ordering cost where a price-dependent (s, S) policy is optimal. We present a numerical study on the sensitivity of optimal profit to various parameters of the operational setting and stochastic price process such as price volatility, customer sensitivity to price changes etc. We then make a comparison with a corresponding discrete-time benchmark model that ignores within-period price fluctuations and present the optimality gap when using the benchmark model as an approximation.Publication Open Access Analysis of a group purchasing organization under demand and price uncertainty(Springer, 2018) Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karabağ, Oktay; Faculty Member; Resercher; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; N/ABased on an industrial case study, we present a stochastic model of a supply chain consisting of a set of buyers and suppliers and a group purchasing organization (GPO). The GPO combines orders from buyers in a two-period model. Demand and price in the second period are random. An advance selling opportunity is available to all suppliers and buyers in the first-period market. Buyers decide how much to buy through the GPO in the first period and how much to procure from the market at a lower or higher price in the second period. Suppliers determine the amount of capacity to sell through the GPO in the first period and to hold in reserve in order to meet demand in the second period. The GPO conducts a uniform-price reverse auction to select suppliers and decides on the price that will be offered to buyers to maximize its profit. By determining the optimal decisions of buyers, suppliers, and the GPO, we answer the following questions: Do suppliers and buyers benefit from working with a GPO? How do the uncertainty in demand, the share of GPO orders in the advance sales market, and the uncertainty in price influence the players' decisions and profits? What are the characteristics of an environment that would encourage suppliers and buyers to work with a GPO? We show that a GPO helps buyers and suppliers to mitigate demand and price risks effectively while collecting a premium by serving as an intermediary between them.