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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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    References listed on IDEAS

    as
    1. Amit Kumar Bairwa & Sandeep Joshi & Dilbag Singh, 2021. "Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, June.
    2. Ming Liu & Xin Liu & Feng Chu & Feifeng Zheng & Chengbin Chu, 2020. "Robust disassembly line balancing with ambiguous task processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 58(19), pages 5806-5835, October.
    3. Mehmet Ali Ilgin & Hakan Akçay & Ceyhun Araz, 2017. "Disassembly line balancing using linear physical programming," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6108-6119, October.
    4. Ali Koc & Ihsan Sabuncuoglu & Erdal Erel, 2009. "Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph," IISE Transactions, Taylor & Francis Journals, vol. 41(10), pages 866-881.
    5. Süleyman Mete & Zeynel Abidin Çil & Eren Özceylan & Kürşad Ağpak & Olga Battaïa, 2018. "An optimisation support for the design of hybrid production lines including assembly and disassembly tasks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(24), pages 7375-7389, December.
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