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

Yaping Fu, Meng Chu Zhou, Xiwang Guo, Liang Qi, Khaled Sedraoui

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1041-1051
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number2
StatePublished - Feb 1 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


  • Disassembly failure risk
  • disassembly sequence planning (DSP) problem
  • multiobjective multiverse optimization algorithm
  • stochastic simulation


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