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Multiverse Optimization Algorithm for Stochastic Biobjective Disassembly Sequence Planning Subject to Operation Failures
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DOI：10.1109/TSMC.2021.3049323.png)
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摘要
En 中文
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.
Keyword:
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
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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