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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Department: search.filters.department.Department of Industrial EngineeringSubject: search.filters.subject.Operations research and management science

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Now showing 1 - 10 of 27
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    PublicationOpen Access
    The state-dependent M/G/1 queue with orbit
    (Springer, 2018) Baron, Opher; Economou, Antonis; Department of Industrial Engineering; Manou, Athanasia; Faculty Member; Department of Industrial Engineering; College of Engineering
    We consider a state-dependent single-server queue with orbit. This is a versatile model for the study of service systems, where the server needs a non-negligible time to retrieve waiting customers every time he completes a service. This situation arises typically when the customers are not physically present at a system, but they have a remote access to it, as in a call center station, a communication node, etc. We introduce a probabilistic approach for the performance evaluation of this queueing system, that we refer to as the queueing and Markov chain decomposition approach. Moreover, we discuss the applicability of this approach for the performance evaluation of other non-Markovian service systems with state dependencies.
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    PublicationOpen Access
    Modeling and analysis of an auction-based logistics market
    (Elsevier, 2008) Ağralı, Semra; 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; 3579
    We consider a logistics spot market where the transportation orders from a number of firms are matched with two types of carriers through a reverse auction. In the spot market, local carriers compete with in-transit carriers that have lower costs. In order to analyze the effects of implementing a logistics spot market on these three parties: firms, local carriers, and in-transit carriers and also the effects of various system parameters, we develop a two-stage stochastic model. We first model the auction in a static setting and determine the expected auction price based on the number of carriers engaging in the auction and their cost distributions. We then develop a continuous-time Markov chain model to evaluate the performance of the system in a dynamic setting with random arrivals and possible abandonment of orders and carriers. By combining these two models, we evaluate the performance measures such as the expected auction price, price paid to the carriers, distribution of orders between local and in-transit carriers, and expected number of carriers and orders waiting at the logistics center in the long run. We present analytical and computational results related to the performance of the system and discuss operation of such a logistics spot market in Turkey.
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    PublicationOpen Access
    Structural properties of a class of robust inventory and queueing control problems
    (Wiley, 2018) Department of Industrial Engineering; N/A; Örmeci, Lerzan; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 32863; 3579; N/A
    In standard stochastic dynamic programming, the transition probability distributions of the underlying Markov Chains are assumed to be known with certainty. We focus on the case where the transition probabilities or other input data are uncertain. Robust dynamic programming addresses this problem by defining a min-max game between Nature and the controller. Considering examples from inventory and queueing control, we examine the structure of the optimal policy in such robust dynamic programs when event probabilities are uncertain. We identify the cases where certain monotonicity results still hold and the form of the optimal policy is determined by a threshold. We also investigate the marginal value of time and the case of uncertain rewards.
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    PublicationOpen Access
    Managing portfolio of elective surgical procedures: a multidimensional inverse newsvendor problem
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2019) Bavafa, Hessam; Leys, Charles M.; Savin, Sergei; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863
    We consider the problem of allocating daily hospital service capacity among several types of elective surgical procedures in the presence of random numbers of urgent procedures described by arbitrary finite support distributions. Our focus is on the interaction between two major constraining hospital resources: operating room (OR) and recovery bed capacity. In our model, each type of surgical procedure has an associated revenue, stochastic procedure duration, and stochastic length of stay (LOS). We consider arbitrary distributions of procedure and LOS durations and derive a two-moment approximation based on the Central Limit Theorem (CLT) for the total procedure duration and the daily number of occupied beds for a given portfolio of procedures. An important novel element of our model is accounting for correlation among the surgical and patient LOS durations for the procedures performed by the same surgical team. We treat the available OR and recovery bed capacity as nominal, allowing them to be exceeded at a cost. The resulting model is a novel, multidimensional variant of the inverse newsvendor problem, where multiple demand types compete for multiple types of service capacity. We characterize the optimal number of elective procedures for single-specialty hospitals and derive an optimality bound for a "front-end" capacity management approach that focuses exclusively on OR capacity. For a setting with two dominant procedure types, we provide an analytical characterization of the optimal portfolio composition under the condition that the revenue from each procedure is proportional to the expected use of hospital resources. We also derive a general analytical description of the optimal portfolio for an arbitrary number of procedure types. For the general case of an arbitrary number of procedure types in the presence of urgent procedures, we conduct a numerical study using data that we have collected at a medium-sized teaching hospital. Our numerical study illustrates the composition of the optimal portfolios of elective procedures in different practical settings, and it investigates the quality of the CLT-based approximation and the effectiveness of the front-end approach to hospital capacity management.
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    PublicationOpen Access
    Design of balanced energy savings performance contracts
    (Taylor _ Francis, 2020) Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600
    Energy savings performance contracts between the energy users and the energy service companies (ESCO) are used to finance energy efficiency investments by using the future energy savings that will result from these investments. We present an analytical model to characterise the energy savings performance contracts and discuss how the risks of estimating the energy savings affect the energy user and the service provider. This characterisation allows determination of the contract parameters for a balanced contract with the information about the energy savings that are expected from the planned energy-efficiency investments. Since it is difficult to get the statistical information about the energy savings before investing in an energy-efficiency project, we develop a distribution-free contract that sets the guaranteed energy savings level based on the mean and the standard deviation of the energy savings and the profit-sharing ratio between the ESCO and the energy user. We show that a simple distribution-free balanced contract performs satisfactorily when the distribution of the energy savings is not known and its mean and the standard deviation are estimated with error. Our analytical results show that the energy savings contracts with the right parameters can mitigate the risks related to realisation of the anticipated energy savings.
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    PublicationOpen Access
    Hub location, routing, and route dimensioning: strategic and tactical intermodal transportation hub network design
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2021) Yaman Hande; Karaşan Oya Ekin; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights.
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    PublicationOpen Access
    Distributionally robust optimization under a decision-dependent ambiguity set with applications to machine scheduling and humanitarian logistics
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2022) Noyan, Nilay; Lejeune, Miguel; Department of Industrial Engineering; Rudolf, Gabor; Faculty Member; Department of Industrial Engineering; College of Engineering
    We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover's distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main computational challenges in solving the problems of interest and provide an overview of various settings leading to tractable formulations. Some of the arising side results, such as the mathematical programming expressions for robustified risk measures in a discrete space, are also of independent interest. Finally, we rely on state-of-the-art modeling techniques from machine scheduling and humanitarian logistics to arrive at potentially practical applications, and present a numerical study for a novel risk-averse scheduling problem with controllable processing times. Summary of Contribution: In this study, we introduce a new class of optimization problems that simultaneously address distributional and decision-dependent uncertainty. We present a unified modeling framework along with a discussion on possible ways to specify the key model components, and discuss the main computational challenges in solving the complex problems of interest. Special care has been devoted to identifying the settings and problem classes where these challenges can be mitigated. In particular, we provide model reformulation results, including mathematical programming expressions for robustified risk measures, and describe how these results can be utilized to obtain tractable formulations for specific applied problems from the fields of humanitarian logistics and machine scheduling. Toward demonstrating the value of the modeling approach and investigating the performance of the proposed mixed-integer linear programming formulations, we conduct a computational study on a novel risk-averse machine scheduling problem with controllable processing times. We derive insights regarding the decision-making impact of our modeling approach and key parameter choices.
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    PublicationOpen Access
    Regenerator location problem in flexible optical networks
    (Informs, 2017) Karasan, Oya Ekin; Department of Industrial Engineering; Yıldız, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; 258791
    In this study, we introduce the regenerator location problem in flexible optical networks. With a given traffic demand, the regenerator location problem in flexible optical networks considers the regenerator location, routing, bandwidth allocation, and modulation selection problems jointly to satisfy data transfer demands with the minimum cost regenerator deployment. We propose a novel branch-and-price algorithm for this challenging problem. Using real-world network topologies, we conduct extensive numerical experiments to both test the performance of the proposed solution methodology and evaluate the practical benefits of flexible optical networks. In particular, our results show that, making routing, bandwidth allocation, modulation selection, and regenerator placement decisions in a joint manner, it is possible to obtain drastic capacity enhancements when only a very modest portion of the nodes is endowed with the signal regeneration capability.
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    PublicationOpen Access
    Agricultural planning of annual plants under demand, maturation, harvest, and yield risk
    (Elsevier, 2012) Department of Industrial Engineering; Tan, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Administrative Sciences and Economics; N/A; 28600
    In this study we present a planning methodology for a firm whose objective is to match the random supply of annual premium fruits and vegetables from a number of contracted farms and the random demand from the retailers during the planning period. The supply uncertainty is due to the uncertainty of the maturation time, harvest time, and yield. The demand uncertainty is the uncertainty of weekly demand from the retailers. We provide a planning methodology to determine the farm areas and the seeding times for annual plants that survive for only one growing season in such a way that the expected total profit is maximized. Both the single period and the multi period cases are analyzed depending on the type of the plant. The performance of the solution methodology is evaluated by using numerical experiments. These experiments show that the proposed methodology matches random supply and random demand in a very effective way and improves the expected profit substantially compared to the planning approaches where the uncertainties are not taken into consideration. (c) 2012 Elsevier B.V. All rights reserved.
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    PublicationOpen Access
    Inequity-averse shelter location for disaster preparedness
    (Taylor _ Francis, 2019) Gutjahr, Walter J.; Department of Industrial Engineering; Salman, Fatma Sibel; Hashemian, Mohammadmahdi; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838; N/A
    We study the problem of selecting a set of shelter locations in preparation for natural disasters. Shelters provide victims of a disaster both a safe place to stay and relief necessities such as food, water and medical support. Individuals from the affected population living in a set of population points go to, or are transported to the assigned open shelters. We aim to take both efficiency and inequity into account, thus we minimize a linear combination of: (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them; and (ii) Gini's Mean Absolute Difference of these distances. We develop a stochastic programming model with a set of scenarios that consider uncertain demand and disruptions in the transportation network. A chance constraint is defined on the total cost of opening the shelters and their capacity expansion. In this stochastic context, a weighted mean of the so-called ex ante and ex post versions of the inequity-averse objective function under uncertainty is optimized. Since the model can be solved to optimality only for small instances, we develop a tailored Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem to solve this problem heuristically for larger instances. We compare the performance of the mathematical program and the GA via benchmark instances where the model can be solved to optimality or near optimality. It turns out that the GA yields small optimality gaps in much shorter time for these instances. We run the GA also on Istanbul data to drive insights to guide decision-makers for preparation.