Mixed-integer programming model and hybrid local search genetic algorithm for human-robot collaborative disassembly line balancing problem

• Published in: International journal of production research Vol. 62; no. 5; pp. 1758 - 1782
• Main Authors: Wu, Tengfei, Zhang, Zeqiang, Zeng, Yanqing, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.03.2024; Taylor & Francis LLC
• Subjects: Balancing; Collaboration; disassembly line balancing; Dismantling; Genetic algorithms; Human-robot collaborative; hybrid local search genetic algorithm; Integer programming; Linear programming; Mixed integer; mixed-integer programming; Remanufacturing; Robots; Search methods; Smoothness; Workstations
• ISSN: 0020-7543; 1366-588X; 1366-588X
• DOI: 10.1080/00207543.2023.2201352

Human-robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human-robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained.