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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 data-driven optimization framework for routing mobile medical facilities(2020) Yücel, Eda; Bozkaya, Burçin; Gökalp, Cemre; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage.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 Open Access A multiperiod stochastic production planning and sourcing problem with service level constraints(Springer, 2005) Yıldırım, Işıl; Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; 3579We study a stochastic multiperiod production planning and sourcing problem of a manufacturer with a number of plants and/or subcontractors. Each source, i.e. each plant and subcontractor, has a different production cost, capacity, and lead time. The manufacturer has to meet the demand for different products according to the service level requirements set by its customers. The demand for each product in each period is random. We present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce, where to produce, how much inventory to carry, etc. This methodology is based on a mathematical programming approach. The randomness in demand and related probabilistic service level constraints are integrated in a deterministic mathematical program by adding a number of additional linear constraints. Using a rolling horizon approach that solves the deterministic equivalent problem based on the available data at each time period yields an approximate solution to the original dynamic problem. We show that this approach yields the same result as the base stock policy for a single plant with stationary demand. For a system with dual sources, we show that the results obtained from solving the deterministic equivalent model on a rolling horizon gives similar results to a threshold subcontracting policy.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 Analysis of copositive optimization based linear programming bounds on standard quadratic optimization(Springer, 2015) Department of Industrial Engineering; Sağol, Gizem; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of EngineeringThe problem of minimizing a quadratic form over the unit simplex, referred to as a standard quadratic optimization problem, admits an exact reformulation as a linear optimization problem over the convex cone of completely positive matrices. This computationally intractable cone can be approximated in various ways from the inside and from the outside by two sequences of nested tractable convex cones of increasing accuracy. In this paper, we focus on the inner polyhedral approximations due to YA +/- ldA +/- rA +/- m (Optim Methods Softw 27(1):155-173, 2012) and the outer polyhedral approximations due to de Klerk and Pasechnik (SIAM J Optim 12(4):875-892, 2002). We investigate the sequences of upper and lower bounds on the optimal value of a standard quadratic optimization problem arising from these two hierarchies of inner and outer polyhedral approximations. We give complete algebraic descriptions of the sets of instances on which upper and lower bounds are exact at any given finite level of the hierarchy. We identify the structural properties of the sets of instances on which upper and lower bounds converge to the optimal value only in the limit. We present several geometric and topological properties of these sets. Our results shed light on the strengths and limitations of these inner and outer polyhedral approximations in the context of standard quadratic optimization.Publication Metadata only Assessing the reliability and the expected performance of a network under disaster risk(Springer, 2011) Günneç, Dilek; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838In a disaster situation, functionality of an infrastructure network is critical for effective emergency response. We evaluate several probabilistic measures of connectivity and expected travel time/distance between critical origin-destination pairs to assess the functionality of a given transportation network in case of a disaster. The input data include the most likely disaster scenarios as well as the probability that each link of the network fails under each scenario. Unlike most studies that assume independent link failures, we model dependency among link failures and propose a novel dependency model that incorporates the impact of the disaster on the network and at the same time yields tractable cases for the computation of the probabilistic measures. We develop algorithms for the computation of the measures and utilize a Monte Carlo simulation algorithm for the intractable cases. We present a case study of the Istanbul highway system under earthquake risk, and compare different dependency structures computationally.Publication Open Access Assessment of quarter billion primary care prescriptions from a nationwide antimicrobial stewardship program(Nature Publishing Group (NPG), 2021) Aksoy, Mesil; İşli, Fatma; Gürpınar, Umut Emre; Göbel, Pınar; Gürsöz, Hakkı; Department of Industrial Engineering; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 110398; 237468We described the significance of systematic monitoring nationwide antimicrobial stewardship programs (ASPs) in primary care. All the prescriptions given by family physicians were recorded in Prescription Information System established by the Turkish Medicines and Medical Devices Agency of Ministry of Health. We calculated, for each prescription, ""antibiotics amount"" as number of boxes times number of items per box for medicines that belong to antiinfectives for systemic use (i.e., J01 block in the Anatomical Therapeutic Chemical Classification System). We compared the antibiotics amount before (2015) and after (2016) the extensive training programs for the family physicians. We included 266,389,209 prescriptions from state-operated family healthcare units (FHUs) between January 1, 2015 and December 31, 2016. These prescriptions were given by 26,313 individual family physicians in 22,518 FHUs for 50,713,181 individual patients. At least one antimicrobial was given in 37,024,232 (28.31%) prescriptions in 2015 and 36,154,684 (26.66%) prescriptions in 2016. The most common diagnosis was ""acute upper respiratory infections (AURI)"" (i.e., J00-J06 block in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems) with 28.05%. The average antibiotics amount over prescriptions with AURI decreased in 79 out of 81 provinces, and overall rate of decrease in average antibiotics amount was 8.33%, where 28 and 53 provinces experienced decreases (range is between 28.63% and-3.05%) above and below this value, respectively. In the most successful province, the highest decrease in average amount of ""other beta-lactam antibacterials"" per prescription for AURI was 49.63% in January. Computational analyses on a big data set collected from a nationwide healthcare system brought a significant contribution in improving ASPs.Publication Metadata only Bayesian analysis of Markov modulated queues with abandonment(Wiley, 2024) Ay, Atilla; Landon, Joshua; Soyer, Refik; Department of Industrial Engineering; Özekici, Süleyman; Department of Industrial Engineering; College of EngineeringWe consider a Markovian queueing model with abandonment where customer arrival, service and abandonment processes are all modulated by an external environmental process. The environmental process depicts all factors that affect the exponential arrival, service, and abandonment rates. Moreover, the environmental process is a hidden Markov process whose true state is not observable. Instead, our observations consist only of customer arrival, service, and departure times during some period of time. The main objective is to conduct Bayesian analysis in order to infer the parameters of the stochastic system, as well as some important queueing performance measures. This also includes the unknown dimension of the environmental process. We illustrate the implementation of our model and the Bayesian approach by using simulated and actual data on call centers.Publication Metadata only Berth and quay crane allocation: a moldable task scheduling model(Taylor & Francis, 2011) Blazewicz, Jacek; Cheng, T. C. E.; Machowiak, Maciej; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033We study the problem of allocating berths to incoming ships and assigning the necessary quay cranes to the ships at a port container terminal. We formulate the problem as the moldable task scheduling problem by considering the tasks as ships and processors as quay cranes assigned to the ships based on the observation that the berthing duration of a ship depends on the number of quay cranes allocated to it. In the model, the processing speed of a task is considered to be a non-linear function of the number of processors allocated to it. We present a suboptimal algorithm that obtains a feasible solution to the discrete version of the problem from the continuous version, that is, where the tasks may require fractional quantities of the resources. We conducted computational experiments to evaluate the performance of the algorithm. The computational results show that the average behaviour of the algorithm is very good.