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

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 2; pp. 1041 - 1051
• Main Authors: Fu, Yaping, Zhou, MengChu, Guo, Xiwang, Qi, Liang, Sedraoui, Khaled
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TSMC.2021.3049323

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.