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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities(Elsevier Science Inc, 2024) Coşkun, Abdullah; Department of Industrial Engineering; Salman, Fatma Sibel; Pashapour, Amirreza; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and EngineeringPre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable;therefore, stockpiled items may not be used. To avoid overstocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.Publication Metadata only Fair and effective vaccine allocation during a pandemic(Elsevier Science Ltd, 2024) Erdoğan, Güneş; Yücel, Eda; Department of Industrial Engineering; Kiavash, Parinaz; Salman, Fatma Sibel; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of EngineeringThis paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well -established Susceptible-Infected-Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed -integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease.Publication Metadata only Constraint programming approach to quay crane scheduling problem(Pergamon-Elsevier Science Ltd, 2013) N/A; N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 328856; 6033This study presents a constraint programming (CP) model for the quay crane scheduling problem (QCSP), which occurs at container terminals, with realistic constraints such as safety margins, travel times and precedence relations. Next, QCSP with time windows and integrated crane assignment and scheduling problem, are discussed. The performance of the CP model is compared with that of algorithms presented in QCSP literature. The results of the computational experiments indicate that the CP model is able to produce good results while reducing the computational time, and is a robust and flexible alternative for different types of crane scheduling problems.Publication Metadata only Modeling mobile health service delivery to Syrian migrant farm workers using call record data(Elsevier, 2021) Yücel, Eda; Coşkun, Abdullah; Department of Industrial Engineering; N/A; Department of International Relations; Salman, Fatma Sibel; Kayı, İlker; Alışık, Sedef Turper; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of International Relations; College of Engineering; School of Medicine; College of Administrative Sciences and Economics; 178838; 168599; 128176A significant number of Syrian refugees under temporary protection in Turkey work in agriculture seasonally in various rural areas during several months a year. These migrant farm workers and their families are deprived of access to the regular health care system and preventive services due to their remote locations. The government supports the delivery of different types of mobile health care services, such as vaccination for children, reproductive health and screening services. While planning the mobile health care service delivery, it is critical to know where the refugees will work during what time frame; hence the demand for the services. By analyzing the call record data of a major mobile network operator in Turkey, we quantify the increase in the volume of calls made by Syrian refugees in various agricultural areas during the harvesting season of local crops. This information helps us to forecast spatial and temporal distribution of demand for mobile health care services at a fine granularity. Taking demand over multiple periods as input into a mathematical programming model, we optimize the routing of mobile clinics that visit locations close to where refugees are concentrated over the given planning horizon. We consider three hierarchical objectives. Given the availability of a number of mobile clinics at community health centers in the districts, the first objective aims to maximize the percentage of refugees that can benefit from each service type within pre-defined close distances. The second objective minimizes the number of clinics needed while covering the maximum percentage of refugees. The third objective minimizes the total travel distance of the clinics, while keeping the maximum coverage level using a minimum number of clinics to achieve this level. We quantify the benefits of centralized planning (by the province directorate) over decentralized planning (by each district separately). We also show the trade-off between the required number of clinics and coverage of potential patients.Publication Metadata only The urban recharging infrastructure design problem with stochastic demands and capacitated charging stations(Pergamon-Elsevier Science Ltd, 2019) Olcaytu, Evren; Sen, Ahmet; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791In this study we develop an exact solution method to optimize the location and capacity of charging stations to satisfy the fast charging needs of electric vehicles in urban areas. Stochastic recharge demands, capacity limitations of charging stations and drivers' route preferences (deviation tolerances) are simultaneously considered to address this challenging problem faced by recharging infrastructure planners or investors. Taking a scenario based approach to model demand uncertainty, we first propose a compact two stage stochastic programming formulation. We then project out the second stage decision variables from the compact formulation by describing the extreme rays of its polyhedral cone and obtain (1) a cut formulation that enables an efficient branch and cut algorithm to solve large problem instances (2) a novel characterization for feasible solutions to the capacitated covering problems. We test our algorithm on the Chicago metropolitan area network, by considering real world origin-destination trip data to model charging demands. Our results attest the efficiency of the proposed branch and cut algorithm and provide significant managerial insights.Publication Metadata only Effects of system parameters on the optimal cost and policy in a class of multidimensional queueing control problems(The Institute for Operations Research and the Management Sciences (INFORMS), 2018) Vercraene, Samuel; Gayon, Jean-Philippe; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579We consider a class of Markov Decision Processes frequently employed to model queueing and inventory control problems. For these problems, we explore how changes in different system input parameters (transition rates, costs, discount rates etc.) affect the optimal cost and the optimal policy when the state space of the problem is multidimensional. To address a large class of problems, we introduce two generic dynamic programming operators to model different types of controlled events. For these operators, we derive sufficient conditions to propagate monotonicity and supermodularity properties of the value function. These properties allow to predict how changes in system input parameters affect the optimal cost and policy. Finally, we explore the case when several parameters are changed at the same time.Publication Metadata only Arc routing problems to restore connectivity of a road network(Pergamon-Elsevier Science Ltd, 2016) Kasaei, Maziar; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.Publication Metadata only Design and operation of intermodal transportation network in the Marmara region of Turkey(Pergamon-Elsevier Science Ltd, 2015) N/A; N/A; Department of Industrial Engineering; Reşat, Hamdi Giray; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956This paper presents a multi-objective optimization model for integrating different transportation modes in the design and operation of an intermodal transportation network in a geographical region. The problem is formulated as a mixed-integer optimization problem that accounts for time and congestion dependent vehicle speeds. We present modeling approach, data analysis and outline the important characteristics of the mathematical programming problem for minimization of transportation cost and time simultaneously by using the augmented epsilon-constraint method. The proposed approach is illustrated on a real world case using data from Marmara region where approximately 50% industrial goods and services in Turkey are produced.Publication Metadata only An online optimization approach to post-disaster road restoration(Pergamon-Elsevier Science Ltd, 2021) Akbari, Vahid; Shiri, Davood; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838Natural disasters impact transportation networks adversely and cause road sections to be damaged or blocked. The road network may even become disconnected, impeding accessibility between disaster-stricken areas and critical locations such as hospitals, relief aid depots and transportation hubs. In the immediate response phase, a set of blocked edges should be selected and restored to reconnect the transportation network. While locations of the disrupted roads can be identified using drone or satellite images, an accurate estimation of time to restore a road segment can be carried out only after expert observations on the field. In this article, we study a post-disaster road restoration problem modeled on an undirected edge-weighted graph with k blocked edges, where the unblocking time of a blocked edge is revealed online once the road restoration team visits an end-node of that blocked edge. The objective is to minimize the time at which the road network is reconnected. We first investigate the worst-case performance of online algorithms against offline optimal solutions by means of the competitive ratio. We prove that any online deterministic algorithm cannot achieve a competitive ratio better than 2k-1. We also provide an optimal online algorithm that is proven to achieve this lower bound. In addition, to achieve good performance on realistic instances, we implement an algorithm that solves a mixed integer programming model each time new information is revealed. Since model solution is prohibitively time-consuming, we also propose a novel polynomial time online algorithm. We compare these two algorithms with two other benchmark online algorithms on both Istanbul road network instances and several other city instances from the literature. Our experiments show that the proposed polynomial time online algorithm performs superior to the benchmark ones and obtains solutions close to the offline optimum on all the tested instances.Publication Metadata only Relief aid stocking decisions under bilateral agency cooperation(Elsevier Science Inc, 2019) Elmaghraby, Wedad; N/A; N/A; Department of Industrial Engineering; Yorulmaz, Abdullah Coşkun; Karaman, Meryem Müge; Salman, Fatma Sibel; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; School of Medicine; N/A; College of Engineering; 3961; N/A; 178838We aim to quantify the benefits of cooperation between humanitarian relief agencies in terms of stocking decisions. We consider two agencies that stock the same type of relief item at different locations prone to individual disaster risks and agree to transship the shortage amount from available stocks in case of a disaster. We incorporate the disaster risk to the Newsvendor model by conditioning the stock quantity decisions on the event that a major disaster occurs within the lifetime of the stocked relief item. We optimize the stock quantity for each agency in response to the other's quantity and compute a Nash Equilibrium solution numerically. We apply this game theoretic approach to the case of earthquake preparedness in Istanbul to optimize the stocking decisions of an agency for shelter units in cooperation with another agency. We investigate the characteristics of the solutions under various parameter settings and identify cases in which cooperation may be beneficial to one or both of the agencies.