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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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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 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 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.Publication Metadata only A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems(Elsevier Science Bv, 2014) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755Most real-life decision-making activities require more than one objective to be considered. Therefore, several studies have been presented in the literature that use multiple objectives in decision models. In a mathematical programming context, the majority of these studies deal with two objective functions known as bicriteria optimization, while few of them consider more than two objective functions. In this study, a new algorithm is proposed to generate all nondominated solutions for multiobjective discrete optimization problems with any number of objective functions. In this algorithm, the search is managed over (p - 1)-dimensional rectangles where p represents the number of objectives in the problem and for each rectangle two-stage optimization problems are solved. The algorithm is motivated by the well-known epsilon-constraint scalarization and its contribution lies in the way rectangles are defined and tracked. The algorithm is compared with former studies on multiobjective knapsack and multiobjective assignment problem instances. The method is highly competitive in terms of solution time and the number of optimization models solved.Publication Metadata only Overtime scheduling: an application in finite-capacity real-time scheduling(Taylor & Francis, 1996) Department of Business Administration; Akkan, Can; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/ANegotiating and meeting due-times for work-orders is often the most important concern of managers of manufacturing systems. We propose a new approach called overtime scheduling that determines on which work-centres, when and how much overtime is required to meet a requested due-time with minimum overtime cost. This method would be used as a part of a finite-capacity real-time scheduling and planning system. We propose a work-order insertion based approach, where a new work-order is scheduled without substantially changing the schedule of previously scheduled work-orders. Based on this approach, we characterise the solution space and present experimental results on the performances of several heuristics.Publication Metadata only Call centers with delay information: models and insights(Informs, 2011) Jouini, Oualid; Dallery, Yves; Department of Business Administration; Karaesmen, Zeynep Akşin; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 4534In this paper, we analyze a call center with impatient customers. We study how informing customers about their anticipated delays affects performance. Customers react by balking upon hearing the delay announcement and may subsequently renege, particularly if the realized waiting time exceeds the delay that has originally been announced to them. The balking and reneging from such a system are a function of the delay announcement. Modeling the call center as an M/M/s+M queue with endogenized customer reactions to announcements, we analytically characterize performance measures for this model. The analysis allows us to explore the role announcing different percentiles of the waiting time distribution, i.e., announcement coverage, plays on subsequent performance in terms of balking and reneging. Through a numerical study, we explore when informing customers about delays is beneficial and what the optimal coverage should be in these announcements. We show how managers of a call center with delay announcements can control the trade-off between balking and reneging through their choice of announcements to be made.Publication Metadata only An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem(Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/AWe study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.