Stochastic Hybrid Discrete Grey Wolf Optimizer for Multi-Objective Disassembly Sequencing and Line Balancing Planning in Disassembling Multiple Products

Xiwang Guo, Zhiwei Zhang, Liang Qi, Shixin Liu, Ying Tang, Ziyan Zhao

Research output: Contribution to journalArticlepeer-review

77 Scopus citations

Abstract

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach.

Original languageEnglish (US)
Pages (from-to)1744-1756
Number of pages13
JournalIEEE Transactions on Automation Science and Engineering
Volume19
Issue number3
DOIs
StatePublished - Jul 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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