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Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations

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  • Fang, Yilin
  • Liu, Quan
  • Li, Miqing
  • Laili, Yuanjun
  • Pham, Duc Truong

Abstract

In the remanufacturing industries, automated disassembly becomes one of the most promising solution in achieving economic benefit. Robotic disassembly line balancing is a key problem that enables automated disassembly to be implemented at industrial scale. This paper focuses on evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. In each workstation, multiple skilled robots perform different tasks belonging to the different end-of-life products or subassemblies simultaneously. Based on the transformed AND/OR graph and parallel disassembly, a mathematical programming model is proposed to minimize the cycle time, the total energy consumption, the peak workstation energy consumption, and the number of robots being used simultaneously. Furthermore, a problem knowledge-leveraging evolutionary algorithm, including encoding/decoding scheme, initialization approach and problem-specific variation operators, is developed to deal with the above problem. Comprehensive experiments are conducted based on 8 product models and 63 problem instances generated in this study. In particular, a comparative study of our proposed algorithm and 5 representative evolutionary algorithms selected from the 3 classes of approaches of dealing with many-objective problems are provided. Then some insights with respect to the design of evolutionary algorithms for our problem are gained from the investigation.

Suggested Citation

  • Fang, Yilin & Liu, Quan & Li, Miqing & Laili, Yuanjun & Pham, Duc Truong, 2019. "Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations," European Journal of Operational Research, Elsevier, vol. 276(1), pages 160-174.
    • Handle: RePEc:eee:ejores:v:276:y:2019:i:1:p:160-174
    DOI: 10.1016/j.ejor.2018.12.035
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    1. Gongdan Xu & Zhiwei Zhang & Zhiwu Li & Xiwang Guo & Liang Qi & Xianzhao Liu, 2023. "Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    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. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    4. 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.
    5. Yusha Zhou & Xiuping Guo & Dong Li, 2022. "A dynamic programming approach to a multi-objective disassembly line balancing problem," Annals of Operations Research, Springer, vol. 311(2), pages 921-944, April.
    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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