IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i2p256-d1566686.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/2/256/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/2/256/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Süleyman Mete & Faruk Serin & Zeynel Abidin Çil & Erkan Çelik & Eren Özceylan, 2023. "A comparative analysis of meta-heuristic methods on disassembly line balancing problem with stochastic time," Annals of Operations Research, Springer, vol. 321(1), pages 371-408, February.
    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. Peng Hu & Feng Chu & Yunfei Fang & Peng Wu, 2022. "Novel distribution-free model and method for stochastic disassembly line balancing with limited distributional information," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1423-1446, July.
    4. 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.
    5. 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).
    6. 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.
    7. Shuangqing Chen & Shanlong Wang & Minghu Jiang & Yuchun Li & Lan Meng & Bing Guan & Ze Yu, 2024. "Layout Reconstruction Optimization Method of Oil-Gathering Systems for Oilfields in the Mid to Late Stage of Development Based on the Arithmetic–Fireworks Optimization Algorithm," Mathematics, MDPI, vol. 12(18), pages 1-39, September.
    8. Bentaha, Mohand-Lounes & Voisin, Alexandre & Marangé, Pascale, 2020. "A decision tool for disassembly process planning under end-of-life product quality," International Journal of Production Economics, Elsevier, vol. 219(C), pages 386-401.
    9. Tao Yin & Yuanzhi Wang & Shixi Cai & Yuxun Zhang & Jianyu Long, 2024. "Unified Modeling and Multi-Objective Optimization for Disassembly Line Balancing with Distinct Station Configurations," Mathematics, MDPI, vol. 12(17), pages 1-24, September.
    10. Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2019. "A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    11. Can B. Kalayci & Olcay Polat & Surendra M. Gupta, 2016. "A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem," Annals of Operations Research, Springer, vol. 242(2), pages 321-354, July.
    12. Devika Kannan & Kiran Garg & P. C. Jha & Ali Diabat, 2017. "Integrating disassembly line balancing in the planning of a reverse logistics network from the perspective of a third party provider," Annals of Operations Research, Springer, vol. 253(1), pages 353-376, June.
    13. Ziyan Zhao & Pengkai Xiao & Jiacun Wang & Shixin Liu & Xiwang Guo & Shujin Qin & Ying Tang, 2023. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    14. Rapeepan Pitakaso & Kanchana Sethanan & Ganokgarn Jirasirilerd & Paulina Golinska-Dawson, 2023. "A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine’s types," Annals of Operations Research, Springer, vol. 324(1), pages 1501-1525, May.
    15. Qi Zhang & Yang Xing & Man Yao & Jiacun Wang & Xiwang Guo & Shujin Qin & Liang Qi & Fuguang Huang, 2024. "An Improved Discrete Bat Algorithm for Multi-Objective Partial Parallel Disassembly Line Balancing Problem," Mathematics, MDPI, vol. 12(5), pages 1-22, February.
    16. Jia Liu & Shuwei Wang, 2017. "Balancing Disassembly Line in Product Recovery to Promote the Coordinated Development of Economy and Environment," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    17. 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.
    18. Mohamed Abdel-Basset & Reda Mohamed & Victor Chang, 2021. "An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 14(21), pages 1-23, November.
    19. Xiaojie Liu & Xuejian Gong & Roger J. Jiao, 2022. "Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    20. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:256-:d:1566686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.