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Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

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  • Yilin Fang
  • Hao Ming
  • Miqing Li
  • Quan Liu
  • Duc Truong Pham

Abstract

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.

Suggested Citation

  • Yilin Fang & Hao Ming & Miqing Li & Quan Liu & Duc Truong Pham, 2020. "Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 846-862, February.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:3:p:846-862
    DOI: 10.1080/00207543.2019.1602290
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    Citations

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    Cited by:

    1. 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.
    2. Kaibo Liang & Li Zhou & Jianglong Yang & Huwei Liu & Yakun Li & Fengmei Jing & Man Shan & Jin Yang, 2023. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System," Mathematics, MDPI, vol. 11(7), pages 1-29, March.
    3. 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).
    4. 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.
    5. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    6. Eduardo Álvarez-Miranda & Jordi Pereira & Harold Torrez-Meruvia & Mariona Vilà, 2021. "A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations," Mathematics, MDPI, vol. 9(17), pages 1-19, September.

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