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Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

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Listed:
  • Ziyan Zhao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Pengkai Xiao

    (Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China)

  • Jiacun Wang

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

  • Shixin Liu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Xiwang Guo

    (Information and Control Engineering College, Liaoning Petrochemical University, Fushun 113001, China)

  • Shujin Qin

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

  • Ying Tang

    (Electrical & Computer Engineering Department, Rowan University, Glassboro, NJ 08028, USA)

Abstract

Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:9-:d:1303455
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    References listed on IDEAS

    as
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    2. Hernán Peraza-Vázquez & Adrián F. Peña-Delgado & Gustavo Echavarría-Castillo & Ana Beatriz Morales-Cepeda & Jonás Velasco-Álvarez & Fernando Ruiz-Perez, 2021. "A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-19, September.
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    4. 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.
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    6. 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.
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