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Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures

Author

Listed:
  • Gongdan Xu

    (Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China)

  • Zhiwei Zhang

    (College of Computer and Communication Engineering, Liaoning Petrochemical University, Fushun 113001, China)

  • Zhiwu Li

    (Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China)

  • Xiwang Guo

    (College of Computer and Communication Engineering, Liaoning Petrochemical University, Fushun 113001, China)

  • Liang Qi

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

  • Xianzhao Liu

    (Hitachi Building Technology (Guangzhou) Co., Ltd., Guangzhou 510670, China)

Abstract

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1557-:d:1104596
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    References listed on IDEAS

    as
    1. Yuhui Shi, 2011. "An Optimization Algorithm Based on Brainstorming Process," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 35-62, October.
    2. 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.
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    Cited by:

    1. Tiewu Xiang & Xinyi Jiang & Guifang Qiao & Chunhui Gao & Hongfu Zuo, 2023. "Kinematics Parameter Calibration of Serial Industrial Robots Based on Partial Pose Measurement," Mathematics, MDPI, vol. 11(23), pages 1-18, November.
    2. 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.
    3. 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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