• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.369-374
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• 2003

Disassembly scheduling is the problem or determining the ordering and disassembly schedules or used or end-of-life products while satisfying the demand or their parts or components over a certain planning horizon. To represent and optimally solve the basic case. i e ., single product type without parts commonality, an Integer programming model is suggested for the objective or minimizing the sum or purchase, setup, inventory holding, and disassembly operation costs. Then, the basic model is offended to consider the cases or single and multiple product types with parts commonality. Computational experiments done on the examples obtained from the literature show that the integer programming approach suggested in this paper works well.

• Management Science and Financial Engineering
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• v.11 no.1
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• pp.63-78
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• 2005

Disassembly scheduling is the problem of determining the ordering and disassembly schedules of used or end-of-life products while satisfying the demand of their parts or components over a certain planning horizon. This paper considers the case of the assembly product structure for the cost-based objective of minimizing the sum of purchase, setup, inventory holding, and disassembly operation costs. To represent and solve the problem optimally, this paper presents an integer programming model, which is a reversed form of the multi-level lot sizing formulation. Computational experiments on an example derived from the literature and a number of randomly generated test problems are done and the results are reported.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.686-692
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• 2002

Disassembly scheduling is the problem of determining the ordering and disassembly schedules of used products while satisfying the demand of their parts of components over a certain planning horizon. The objective is to minimize the sum of purchase, setup, disassembly operation and inventory holding costs. This paper considers products with assembly structure, i.e. products without parts commonality, and suggests a heuristic in which an initial solution is obtained in the form of the minimal latest disassembly schedule, and then improved considering trade-offs among different cost factors. To show the performance of the heuristic suggested in this paper, computational experiments were done on the modified existing examples and the results show that the heuristic can give optimal of very near optimal solutions within very short computation times.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.585-597
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• 2008

The aim of this study was to find an analytic solution to the problem of determining the optimal capacity (lot-size) of a batch-storage network to meet demand for a finished product in a system undergoing random failures of operating time and/or batch material. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to short-term random variations in the cycle time and batch size as well as long-term variations in the average trend. Some of the production processes have random variations in product quantity. The spoiled materials are treated through regeneration or waste disposal processes. All other processes have random variations only in the cycle time. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis, the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced.