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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Hub network design problem with capacity, congestion, and stochastic demand considerations(Informs, 2023) Bayram, Vedat; Farham, M. Saleh; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of EngineeringOur study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.Publication Metadata only Sustainability analysis of cement supply chains considering economic, environmental and social effects(Elsevier, 2023) Suhaib, Seyyed Amir Babak; Rasmi, Seyyed Amir Babak; Department of Industrial Engineering; Türkay, Metin; Department of Industrial Engineering; College of EngineeringCement is a fundamental ingredient in the construction industry and infrastructure development; these sectors depend on this raw material and the demand proportionally increases as the population of the world grows and the urbanization rate accelerates. Despite being a vital element of the development, cement manufacturing sector is a major source of GHG emissions and depletes the natural capital. In this paper we examine the effects of incorporating sustainability indicators in cement supply chains under the Triple Bottom Line (TBL) accounting of sustainability using multi-Objective optimization. We implement a tailored multi-objective optimization algorithm that generates unique optimal solutions hence giving an accurate and well-defined Pareto front to decision makers. Our model shows that even by including additional environmental and social considerations cement manufacturing is economically feasible.Publication Metadata only Modeling strategic walk-in patients in appointment systems: equilibrium behavior and capacity allocation(Elsevier, 2024) Department of Business Administration;Department of Industrial Engineering; Örmeci, Lerzan; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; College of EngineeringWe consider an outpatient clinic with strategic patients who choose between making an appointment with an indirect wait cost (advance patients) and walking in with an inconvenience cost that includes the risk of being rejected and waiting in the clinic (walk-ins). Patients have different indirect waiting costs and show up with some probability. The clinic allocates slots to advance and walk-in patients to minimize the expected blockage of walk-in patients. We characterize the equilibrium behavior of patients and investigate the optimal capacity allocation, for unobservable (patients know the expected waiting time) and observable (patients know their exact waiting time) cases. For the unobservable case, one of the three options is optimal: allocating all slots to advance patients, allocating all slots to walk-ins, or allocating a certain number of slots to advance patients so that only urgent patients would choose the walk-in option. In contrast, for the observable case, no such structure exists. We investigate the value of information numerically. Finally, we develop a simulation platform to examine the ef-fects of model assumptions. We find the optimal capacity allocation for the simulation model to benchmark the performance of the theoretical models and two simple policies. These analyses verify that our models work well in realistic simulations, offering a useful tool in practice. In contrast to the common practice of allocating some slots to walk-ins, our results suggest that the clinics should prefer a system that allocates all slots to advance patients in certain environments due to the strategic behavior of patients.Publication Metadata only Production and energy mode control of a production-inventory system(Elsevier, 2023) Karabag, Oktay; Khayyati, Siamak; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsEnergy efficiency in manufacturing can be improved by controlling energy modes and production dy-namically. We examine a production-inventory system that can operate in Working, Idle, and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival, production and warm-up times, we formulate the problem of de-termining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy, inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival, production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint pro-duction and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary, we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system.Publication Metadata only Energy-efficient production control of a make-to-stock system with buffer- and time-based policies(Taylor and Francis Ltd., 2023) Karabağ, Oktay; Khayyati, Siamak; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsIncreasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance.Publication Metadata only Continuous-flow simulation of manufacturing systems with assembly/disassembly machines, multiple loops and general layout(Elsevier Sci Ltd, 2023) Scrivano, Salvatore; Tolio, Tullio; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsPerformance evaluation methods are important to design and control manufacturing systems. Approximate analytical methods are fast, but they may be limited by the restrictive assumptions on the system. On the contrary, simulation has not specific limitations in its applicability, but the time to model and analyse a manufacturing system can increase as the level of detail addressed by the model increases. The main contribution of this study is presenting a computationally efficient methodology to simulate single-part continuous-flow manufacturing systems with assembly/disassembly machines, multiple loops, general layout and general inter-event time distributions. By using graph theory, a new method is presented to identify the machines causing slowdown, blocking and starvation in a general layout and determine the time before the occurrence of a state transition for each machine and the time before the fulfilment or depletion of each buffer. By advancing the time clock to the next event-time accordingly, the number of discrete events needed to be simulated is decreased compared to a discrete-event simulation with discrete flow of parts. As a result, the proposed method is on average 15 times faster than DES methods in the analysis of discrete-flow systems, and 110 times faster on average in the analysis of continuous-flow systems. The low computational time of the proposed method allows to simulate systems under general assumptions and in a very short time.Publication Metadata only Linear tests for decreasing absolute risk aversion stochastic dominance(The Institute for Operations Research and the Management Sciences (INFORMS), 2015) Fang, Yi; Kopa, Milos; N/A; Post, Gerrit Tjeerd; Other; Graduate School of Business; N/AWe develop and implement linear formulations of convex stochastic dominance relations based on decreasing absolute risk aversion (DARA) for discrete and polyhedral choice sets. Our approach is based on a piecewise-exponential representation of utility and a local linear approximation to the exponentiation of log marginal utility. An empirical application to historical stock market data suggests that a passive stock market portfolio is DARA stochastic dominance inefficient relative to concentrated portfolios of small-cap stocks. The mean-variance rule and Nth-order stochastic dominance rules substantially underestimate the degree of market portfolio inefficiency because they do not penalize the unfavorable skewness of diversified portfolios, in violation of DARA.Publication Metadata only Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming(Springer, 2000) N/A; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755One way of solving multiple objective mathematical programming problems is ending discrete representations of the efficient set. A modified goal of finding good discrete representations of thr efficient set would contribute to the practicality of vector maximization algorithms. We define coverage, uniformity and cardinality as the three attributes of quality of discrete representations and introduce a framework that includes these attributes in which discrete representations can be evaluated, compared to each other, and judged satisfactory or unsatisfactory by a Decision Maker. We provide simple mathematical programming formulation that can he used to compute the coverage error of a given discrete representation. Our formulations are practically implementable when the problem under study is a multiobjective linear programming problem. We believe that the interactive algorithms along with the vector maximization methods can make use of our framework and its tools.Publication Metadata only Bounded rationality in clearing service systems(Elsevier, 2020) Department of Industrial Engineering; Canbolat, Pelin Gülşah; Faculty Member; Department of Industrial Engineering; College of Engineering; 108242This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) for an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) system revenue grows arbitrarily large as customers tend to being irrational, (iii) social welfare is maximized when customers are perfectly rational, (iv) in all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality. (C) 2019 Elsevier B.V. All rights reserved.Publication Metadata only Finite-capacity scheduling-based planning for revenue-based capacity management(Elsevier Science Bv, 1997) Department of Business Administration; Akkan, Can; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/AFinite-capacity scheduling can be argued to be a crucial component of revenue-based capacity management. In that case, one way to plan production is to reserve portions of capacity for incoming customer orders as they arrive, in real-time. In such a planning method, the way these work-orders are scheduled affects the useable capacity, due to fragmentation of the time-line. Assuming the work-orders are rejected if they cannot be inserted into the existing schedule, we develop heuristics to minimise the present-value of the cost of rejecting orders and inventory holding cost due to early completion. We perform simulation experiments to compare the performance of these heuristics in addition to some common heuristics used in practice.