Optimizing a stochastic disassembly line balancing problem with task failure via a hybrid variable neighborhood descent-artificial bee colony algorithm

• Published in: International journal of production research Vol. 61; no. 7; pp. 2307 - 2321
• Main Authors: Guo, Hongfei, Zhang, Linsheng, Ren, Yaping, Li, Yun, Zhou, Zhongwei, Wu, Jianzhao
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.04.2023; Taylor & Francis LLC
• Subjects: artificial bee colony algorithm; Balancing; Disassembly line balancing problem; Dismantling; End of life; expected profit; Failure; Heuristic methods; Mathematical models; Robustness (mathematics); Search algorithms; Swarm intelligence; task failure; variable neighborhood descent method
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2022.2069524

A disassembly line is an effective disassembly system to recover end-of-life products. In real life, as end-of-life products are subject to varying degrees of wear and tear, task failure may occur in the disassembly process. In this paper, the task failure risks are considered, and an expected profit-based stochastic disassembly line balancing problem is studied. First, a mathematical model is presented to maximise the expected recovering profit with task failures. Then, a hybrid metaheuristic approach is developed to efficiently solve the proposed model, which is integrated with a variable neighbourhood descent method and an artificial bee colony algorithm. Finally, the effectiveness and robustness of the proposed algorithm are verified by three cases, and experiment results show that the solution performance of the proposed approach is superior to the other three existing methods.