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An asynchronous parallel disassembly planning based on genetic algorithm

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  • Ren, Yaping
  • Zhang, Chaoyong
  • Zhao, Fu
  • Xiao, Huajun
  • Tian, Guangdong

Abstract

Disassembly is one of the most crucial remanufacturing activities. Disassembly sequence planning (DSP) is a combinatorial optimization problem and has been studied by many researchers. Conventional DSP techniques focus on sequential disassembly planning (SDP) in which only one manipulator is used to remove a single part or subassembly at a time such that it is inefficient when disassembling large or complex products. Recently, parallel disassembly has attracted some interest as it employs several manipulators to remove multiple components simultaneously. However, most of the work to date focuses on parallel disassembly techniques which require synchronization between manipulators, i.e., they must start their tasks simultaneously. This simplifies the modeling and analysis efforts but fails to fully realize the benefits of parallel disassembly. In this work, we propose asynchronous parallel disassembly planning (aPDP) which eliminates the synchronization requirement. In addition to precedence constraints, aPDP becomes highly operation time-dependent. To deal with this, we design an efficient encoding and decoding strategy for the disassembly process. In this paper, a metaheuristic approach, based on a genetic algorithm, is developed to solve the aPDP problem. The proposed algorithm is applied to four products which require disassembly processes of varying complexity, and the results are compared with two methods reported in literature. It is suggested that the proposed approach can identify faster disassembly processes, especially when solving large-scale problems.

Suggested Citation

  • Ren, Yaping & Zhang, Chaoyong & Zhao, Fu & Xiao, Huajun & Tian, Guangdong, 2018. "An asynchronous parallel disassembly planning based on genetic algorithm," European Journal of Operational Research, Elsevier, vol. 269(2), pages 647-660.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:2:p:647-660
    DOI: 10.1016/j.ejor.2018.01.055
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    References listed on IDEAS

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    1. Panwalkar, S.S., 2016. "The proportionate two-machine no-wait job shop scheduling problemAuthor-Name: Koulamas, Christos," European Journal of Operational Research, Elsevier, vol. 252(1), pages 131-135.
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    4. Yaping Ren & Daoyuan Yu & Chaoyong Zhang & Guangdong Tian & Leilei Meng & Xiaoqiang Zhou, 2017. "An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7302-7316, December.
    5. Hyung-Won Kim & Dong-Ho Lee, 2017. "An optimal algorithm for selective disassembly sequencing with sequence-dependent set-ups in parallel disassembly environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7317-7333, December.
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    Cited by:

    1. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    2. Lei, Jieyu & Che, Ada & Van Woensel, Tom, 2024. "Collection-disassembly-delivery problem of disassembly centers in a reverse logistics network," European Journal of Operational Research, Elsevier, vol. 313(2), pages 478-493.
    3. Leilei Meng & Biao Zhang & Yaping Ren & Hongyan Sang & Kaizhou Gao & Chaoyong Zhang, 2022. "Mathematical Formulations for Asynchronous Parallel Disassembly Planning of End-of-Life Products," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
    4. Tamás Kegyes & Zoltán Süle & János Abonyi, 2024. "Disassembly line optimization with reinforcement learning," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(4), pages 1115-1142, December.
    5. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    6. Yaping Ren & Xinyu Lu & Hongfei Guo & Zhaokang Xie & Haoyang Zhang & Chaoyong Zhang, 2023. "A Review of Combinatorial Optimization Problems in Reverse Logistics and Remanufacturing for End-of-Life Products," Mathematics, MDPI, vol. 11(2), pages 1-24, January.

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