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An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems

Author

Listed:
  • Xinshuo Cui

    (College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China)

  • Qingbo Meng

    (College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China)

  • Jiacun Wang

    (Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA)

  • Xiwang Guo

    (College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China)

  • Peisheng Liu

    (College of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China)

  • Liang Qi

    (Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shujin Qin

    (College of Economics and Management, Shangqiu Normal University, Shangqiu 476000, China)

  • Yingjun Ji

    (Faculty of Information, Liaoning University, Shenyang 110036, China)

  • Bin Hu

    (Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA)

Abstract

In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality.

Suggested Citation

  • Xinshuo Cui & Qingbo Meng & Jiacun Wang & Xiwang Guo & Peisheng Liu & Liang Qi & Shujin Qin & Yingjun Ji & Bin Hu, 2025. "An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems," Mathematics, MDPI, vol. 13(2), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:256-:d:1566686
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