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On the end-of-life state oriented multi-objective disassembly line balancing problem

Author

Listed:
  • Lixia Zhu

    (Southwest Jiaotong University
    Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province)

  • Zeqiang Zhang

    (Southwest Jiaotong University
    Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province)

  • Yi Wang

    (Auburn University at Montgomery)

  • Ning Cai

    (Southwest Jiaotong University
    Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province)

Abstract

The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.

Suggested Citation

  • Lixia Zhu & Zeqiang Zhang & Yi Wang & Ning Cai, 2020. "On the end-of-life state oriented multi-objective disassembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1403-1428, August.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-019-01519-3
    DOI: 10.1007/s10845-019-01519-3
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    References listed on IDEAS

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    Cited by:

    1. Junqi Liu & Zeqiang Zhang & Feng Chen & Silu Liu & Lixia Zhu, 2022. "A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 953-972, April.
    2. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    3. Liang, Wei & Zhang, Zeqiang & Yin, Tao & Zhang, Yu & Wu, Tengfei, 2023. "Modelling and optimisation of energy consumption and profit-oriented multi-parallel partial disassembly line balancing problem," International Journal of Production Economics, Elsevier, vol. 262(C).
    4. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. He, Junkai & Chu, Feng & Dolgui, Alexandre & Anjos, Miguel F., 2024. "Multi-objective disassembly line balancing and related supply chain management problems under uncertainty: Review and future trends," International Journal of Production Economics, Elsevier, vol. 272(C).

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