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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Transshipment network design for express air cargo operations in China(Elsevier B.V., 2023) Savelsbergh, Martin; Dogru, Ali K.; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of EngineeringWe introduce a novel multimodal (ground and air transportation) network design model with transshipments for the transport of express cargo with heterogeneous service classes (i.e., next morning delivery, and next day delivery). We formulate this problem using a novel path-based mixed-integer program which seeks to maximize the demand (weight) served. We investigate the value of the proposed transshipment network under various operational conditions and by benchmarking against a direct shipment network and a network with a single transshipment point which mimics a classical star-shaped hub-and-spoke network. Our extensive computational study with real-world data from ShunFeng (SF) Express reveals that the integration of ground and air transportation improves the coverage and that transshipment enables serving a large number of origin–destination pairs with a small number of cargo planes. Importantly, we show that by simplifying handling, i.e., employing cross-docking rather than time-consuming sortation, a transshipment network can transport express cargo fast enough to meet demanding delivery deadlines. Finally, we find that increasing the efficiency of intra-city operations and extending the nightly operating time window are the most effective operational adjustments for further improving the performance of the proposed transshipment network.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 The digital twin synchronization problem: framework, formulations, and analysis(Taylor & Francis Inc, 2023) Matta, Andrea; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsAs the adoption of digital twins increases steadily, it is necessary to determine how to operate them most effectively and efficiently. In this article, the digital twin synchronization problem is introduced and defined formally. Frequent synchronizations would increase cost and data traffic congestion, whereas infrequent synchronizations would increase the bias of the predictions and yield wrong decisions. This work defines the synchronization problem variants in different contexts. To discuss the problem and its solution, the problem of determining when to synchronize an unreliable production system with its digital twin to minimize the average synchronization and bias costs is formulated and analyzed analytically. The state-independent, state-dependent, and full-information solutions have been determined by using a stochastic model of the system. Solving the synchronization problem using simulation is discussed, and an approximate policy is proposed. Our results show that the performance of the state-dependent policy is close to the optimal solution that can be obtained with full information and significantly better than the performance of the state-independent policy. Furthermore, the approximate periodic state-dependent policy yields near-optimal results. To operate digital twins more effectively, the digital twin synchronization problem must be considered and solved to determine the optimal synchronization policy.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 Operational research: methods and applications(Taylor and Francis Ltd., 2024) Petropoulos, Fotios; Laporte, Gilbert; Aktas, Emel; Alumur, Sibel A.; Archetti, Claudia; Ayhan, Hayriye; Battarra, Maria; Bennell, Julia A.; Bourjolly, Jean-Marie; Boylan, John E.; Breton, Michèle; Canca, David; Charlin, Laurent; Chen, Bo; Cicek, Cihan Tugrul; Cox, Louis Anthony; Currie, Christine S.M.; Demeulemeester, Erik; Ding, Li; Disney, Stephen M.; Ehrgott, Matthias; Eppler, Martin J.; Erdoğan, Güneş; Fortz, Bernard; Franco, L. Alberto; Frische, Jens; Greco, Salvatore; Gregory, Amanda J.; Hämäläinen, Raimo P.; Herroelen, Willy; Hewitt, Mike; Holmström, Jan; Hooker, John N.; Işık, Tuğçe; Johnes, Jill; Kara, Bahar Y.; Karsu, Özlem; Kent, Katherine; Köhler, Charlotte; Kunc, Martin; Kuo, Yong-Hong; Letchford, Adam N.; Leung, Janny; Li, Dong; Li, Haitao; Lienert, Judit; Ljubić, Ivana; Lodi, Andrea; Lozano, Sebastián; Lurkin, Virginie; Martello, Silvano; McHale, Ian G.; Midgley, Gerald; Morecroft, John D.W.; Mutha, Akshay; Petrovic, Sanja; Pferschy, Ulrich; Psaraftis, Harilaos N.; Rose, Sam; Saarinen, Lauri; Salhi, Said; Song, Jing-Sheng; Sotiros, Dimitrios; Stecke, Kathryn E.; Strauss, Arne K.; Tarhan, İstenç; Thielen, Clemens; Toth, Paolo; Van Woensel, Tom; Berghe, Greet Vanden; Vasilakis, Christos; Vaze, Vikrant; Vigo, Daniele; Virtanen, Kai; Wang, Xun; Weron, Rafał; White, Leroy; Yearworth, Mike; Yıldırım, E. Alper; Zaccour, Georges; Zhao, Xuying; Department of Industrial Engineering; Oğuz, Ceyda; Department of Industrial Engineering; College of EngineeringThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.Publication Metadata only Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system(Elsevier B.V., 2025) Tan, Barış; Department of Business Administration; Özkan, Erhun; Department of Business Administration; College of Administrative Sciences and EconomicsWe study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server's energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments. © 2024