Browsing by Author "Edis, EB"

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    Parallel machine scheduling with flexible resources
    Edis, EB; Oguz, C
    Parallel machine flexible resource scheduling (PMFRS) problems consider an additional flexible resource (e.g. operators), which can be freely allocated to any jobs and/or any machines and may speed-up the process in proportion to its amount. If job-machine assignment is unspecified, the problem is referred to as unspecified PMFRS (UPMFRS). This paper reviews the mathematical models of both PMFRS and UPMFRS problems in the literature and not only gives some extensions to the model of dynamic PMFRS problem but also presents integer programming (IP) models for static and dynamic UPMFRS problems with the objective of minimizing makespan. To solve large-sized dynamic PMFRS and UPMFRS problems, a relaxed IP based constraint programming (CP) approach is also proposed. All IP models and the proposed IP/CP approach are tested with an extensive computational study. The results of the computational experiments are discussed with respect to the major parameters of the problem and conclusions are drawn. (C) 2012 Elsevier Ltd. All rights reserved.
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    Constraint programming approaches to disassembly line balancing problem with sequencing decisions
    Edis, EB
    Recovery of products has received much attention in the last decade due to the increase in both environmental awareness and regulations enacted by governments. In product recovery, disassembly of a product into its constituent parts on a line is one of the most significant operations. This paper deals with a disassembly line balancing and sequencing (DLBS) problem subject to balancing issues, hazardousness of parts, demand quantities and direction changes considered in a lexicographic order. Due to the combinatorial nature of this problem, exact methods, e.g., mixed integer linear programming (MILP), are able to solve only small and medium size problems. Therefore, various metaheuristic algorithms are proposed in literature to find near-optimal solutions. In this paper, constraint programming (CP), which is a suitable technique especially for highly-constrained discrete problems, is used to develop models and solution approaches. To the best of author's knowledge, this study is the first that uses CP for the disassembly line balancing problems. For the DLBS problem, first, a generic CP model is developed. This CP model provides efficient results for small/medium size disassembly problems and benchmark instances. Observing that the generic CP model could not produce even feasible sequence of tasks for some large-sized benchmark instances, a CP-based solution approach is proposed. This approach generates a feasible sequence subject to a fixed assignment of tasks to the workstations by using a CP model and uses this sequence as an initial feasible solution within a warm-start context in CP sequencing models. The computational results show that the proposed CP model improves the several best solutions of medium-sized benchmark instances, while the proposed CP-based solution approach produces excellent results in all large test instances by either improving the best solutions (found so far) or establishing new benchmark solutions. (C) 2020 Elsevier Ltd. All rights reserved.
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    Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions
    Edis, EB; Ilgin, MA; Edis, RS
    Due to the acceleration of technological developments and shortening of product life cycles, product recovery has gained great importance in recent years. Disassembly line balancing (DLB) problem is one of the most important problems encountered during disassembly operations in product recovery. In this study, a single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes is considered. Majority of DLB studies in the literature solve this problem using heuristics or metaheuristics which do not guarantee the optimality. Although a few studies present mathematical formulations for some variants of this problem, they prefer to solve the problem by using heuristics or metaheuristics due to the non-linear structure and combinatorial nature of the problem. In this study, a generic mixed integer linear programming (MILP) model is developed for the investigated problem and its performance is tested through a series of benchmark instances. The computational results demonstrate that the proposed MILP model is able to solve test instances with up to 30 tasks. Hence, it can effectively be utilized to evaluate the optimality performance of DLB approaches. Moreover, several extensions on the MILP model regarding to line balancing, hazardousness and demand of parts and direction changes are proposed and their effects are analyzed through computational studies. (c) 2019 Elsevier Ltd. All rights reserved.
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    Job allocation and scheduling of batch processing ovens: An integer programming model
    Edis, EB; Kuru, B
    This study deals with a job allocation and scheduling problem of batch processing ovens belonging to a factory that manufactures welding electrodes. Each electrode type may not be heated on all of the ovens. Besides, there exists a priority order of ovens for each electrode type. The other inputs are; work order amounts of electrodes, the ready times, heating times, cooling down times, and the capacity of each oven with respect to the electrode types. The work orders with the same heating time may be heated simultaneously without exceeding the oven capacity. For the defined problem, an integer programming model is developed. The objective function is to assign the work orders to the preferred ovens as possible and to maximize the utilization rates of ovens. The model determines which work order will be heated on which machine and in which time interval. The model results are launched for two different work order sets. Finally, by discussing the results, the applicability of the model is demonstrated.
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    Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure
    Edis, EB; Araz, OU; Eski, O; Edis, RS
    Warehouse operations have a significant role to survive in today's competitive world. Hence, companies introduce various solutions to improve efficiency of warehouses which is mainly affected by the performance of storage operations. This study deals with a real-life storage location and assignment problem encountered in a fastener company where several orders consisting steel coils are to be assigned into storage areas. Three individual objective functions; minimizing the number of lanes to be used, minimizing area usage, and maximizing volume utilization are considered. For the investigated problem, first, an integer linear programming (ILP) model is developed. Then, a greedy randomized adaptive search procedure (GRASP) which provides quick and efficient solutions is proposed. The proposed methods are applied to the real problem case and the results are compared with the current storage assignment. Moreover, through an extensive computational study, the performances of proposed methods are evaluated on a set of test problems with different range of characteristics. The computational results show that the ILP model proves optimality in most of the problem instances within reasonable computation times, while the GRASP gives quick solutions with small optimality gaps.
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    Mixed integer programming approaches to partial disassembly line balancing and sequencing problem
    Edis, EB; Edis, RS; Ilgin, MA
    Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e. g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.
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    SOLUTION APPROACHES FOR SIMULTANEOUS SCHEDULING OF JOBS AND OPERATORS ON PARALLEL MACHINES
    Edis, EB; Oguz, C; Özkarahan, I
    In literature, most of the studies related to parallel machine scheduling problems assume that jobs require only machines as the processing resources and accordingly deal with simply job-machine scheduling problem. However, in real-life manufacturing environments, jobs may also require additional resources. A common example of additional resources is cross-training workers that perform tasks related with different machines. This study handles a real-life problem that requires simultaneous scheduling of jobs and operators over the parallel machines. Operators are responsible for monitoring the machines, unloading the parts and trimming extra material. These tasks may not require an operator's full attention during the processing of a job at one machine. In this context, a significant distinguishing feature of the investigated problem is that an operator can be assigned to more than one machine through the specified time periods. While determining the machines that an operator has to deal with during the same scheduling periods, the physical closeness of the machines should also be taken into account. For the problem on hand, with the aim of minimizing the completion time of the last job, integer and constraint programming models have been developed. The models have been evaluated through the test problems with different parameters, and their performances have been discussed. Finally, the applicability of the proposed constraint programming model on the real-life problem has been shown.
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    Parallel machine scheduling with additional resources: Notation, classification, models and solution methods
    Edis, EB; Oguz, C; Ozkarahan, I
    Majority of parallel machine scheduling studies consider machine as the only resource. However, in most real-life manufacturing environments, jobs may require additional resources, such as automated guided vehicles, machine operators, tools, pallets, dies, and industrial robots, for their handling and processing. This paper presents a review and discussion of studies on the parallel machine scheduling problems with additional resources. Papers are surveyed in five main categories: machine environment, additional resource, objective functions, complexity results and solution methods, and other important issues. The strengths and weaknesses of the literature together with open areas for future studies are also emphasized. Finally, extensions of integer programming models for two main classes of related problems are given and conclusions are drawn based on computational studies. (C) 2013 Elsevier B.V. All rights reserved.