期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 52, 期 2, 页码 1041-1051出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2021.3049323
关键词
Optimization; Stochastic processes; Uncertainty; Energy consumption; Hidden Markov models; Task analysis; Planning; Disassembly failure risk; disassembly sequence planning (DSP) problem; multiobjective multiverse optimization algorithm; stochastic simulation
资金
- National Natural Science Foundation of China [61703220, 71871105]
- China Postdoctoral Science Foundation [2019T120569]
- Outstanding Youth Innovation Team Project of Colleges and Universities in Shandong Province [2020RWG011]
- Liaoning Province Education Department Scientific Research Foundation of China [L2019027]
- Liaoning Province Dr. Research Foundation of China [20170520135]
- Liaoning Revitalization Talents Program [XLYC1907166]
- Natural Science Foundation of Shandong Province [ZR2019BF004]
- Deanship of Scientific Research (DSR) at King Abdulaziz University [RG-21-135-38]
Disassembly is a crucial step in remanufacturing process for resource reuse and waste reduction. This study introduces a stochastic biobjective disassembly sequence planning problem to maximize profit and minimize energy consumption. Chance-constrained programming model with multiverse optimization algorithm is proposed for efficient solutions.
Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products' exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.
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