Multiverse Optimization Algorithm for Stochastic Biobjective Disassembly Sequence Planning Subject to Operation Failures

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 ec...

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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Systems Jg. 52; H. 2; S. 1041 - 1051
Hauptverfasser: Fu, Yaping, Zhou, MengChu, Guo, Xiwang, Qi, Liang, Sedraoui, Khaled
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Confidence intervals
Constraint modelling
Disassembly failure risk
disassembly sequence planning (DSP) problem
Disassembly sequences
Dismantling
End of life
Energy consumption
Hidden Markov models
multiobjective multiverse optimization algorithm
Multiple objective analysis
Optimization
Optimization algorithms
Planning
Remanufacturing
Stochastic processes
stochastic simulation
Task analysis
Uncertainty
ISSN:2168-2216, 2168-2232
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2021.3049323