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A sample average approximation algorithm for selective disassembly sequencing with abnormal disassembly operations and random operation times

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Abstract

Selective disassembly sequencing is the problem of determining the sequence of disassembly operations to extract one or more target components of a product. This study addresses a stochastic version of the problem in which abnormal disassembly operations and random operation times are considered under the parallel disassembly environment, i.e., one or more components that can be disassembled further remain after a disassembly operation is done. Abnormal disassembly operations are defined as those in which fasteners can be removed by additional random destructive operations without damaging to target components. After representing all possible sequences using the extended process graph, a stochastic integer programming model is developed that minimizes the sum of disassembly and penalty costs, where the disassembly cost consists of sequence-dependent setup and operation costs, and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a given threshold value. A sample average approximation algorithm is proposed that incorporates a branch and bound algorithm to solve the deterministic problem under a scenario for abnormal operations and operation times optimally. Finally, the algorithm is illustrated with a hand-light example and a larger instance.

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education of Korea Government (grant number: 2015R1D1A1A01057669).

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  1. Department of Industrial Engineering, Hanyang University, Seoul, Republic of Korea

    Hyung-Won Kim & Dong-Ho Lee

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  1. Hyung-Won Kim
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Correspondence to Dong-Ho Lee.

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Kim, HW., Lee, DH. A sample average approximation algorithm for selective disassembly sequencing with abnormal disassembly operations and random operation times. Int J Adv Manuf Technol 96, 1341–1354 (2018). https://doi.org/10.1007/s00170-018-1667-9

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