Disassembly modeling, planning, and leveling for a cam-operated rotary switch assembly: a case study

Vinodh, S.; Kumar, R. Praveen; Nachiappan, N.

doi:10.1007/s00170-011-3826-0

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…Most of the earlier studies consider only one decision variable, for example, Penev and De Ron 17 and Igarashi et al 18 for disassembly levelling; Lambert, 19 Hui et al, 20 Adenso-Diaz et al 21 and Jin et al 22 for disassembly sequencing; and Bufardi et al 23 and Jun et al 24 for determining end-of-life options. As an extension, several articles consider two decision variables at the same time; for example, see Pnueli and Zussman, 25 Johnson and Wang, 26,27 Lambert, 28,29 Kang et al 30,31 and Vinodh et al 32,33 for determining disassembly level and sequence at the same time. Finally, some other articles consider all the three decision variables, for example, see Erdos et al, 34 Teunter, 35 Behdad et al 36 and Ma et al 37 Disassembly lot-sizing As a mid-or short-term planning activity, various researches have been done on disassembly lot-sizing.…”

Section: Disassembly Process Planningmentioning

confidence: 99%

Multi-period disassembly levelling and lot-sizing for multiple product types with parts commonality

Kim

Doh

Lee

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Disassembly levelling is to determine disassembly structures that specify components to be obtained from end-of-use/life products, and disassembly lot-sizing is to determine the timing and quantity of disassembling end-of-use/life products to satisfy the demands of their components. As an extension of the previous studies that consider them separately, this study integrates the two problems, especially in the form of multi-period model. Particularly, this study considers a generalized integrated problem in which disassembly levels may be different for the products of the same type. To describe the problem mathematically, we develop an integer programming model that minimizes the sum of setup, operation and inventory holding costs. Then, due to the problem complexity, a heuristic algorithm is proposed that consists of two phases: (a) constructing an initial solution using a priority-based greedy heuristic and (b) improving it by removing unnecessary disassembly operations after characterizing the properties of the problem. To show the performance of the heuristic algorithm, computational experiments were performed on various test instances and the results are reported.

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