A bi-objective salp swarm algorithm with sine cosine operator for resource constrained multi-manned disassembly line balancing problem

• Published in: Applied soft computing Vol. 131; p. 109759
• Main Authors: Zhou, Binghai, Bian, Jingrao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• Subjects: Epsilon constraint; Line balancing; Mixed-model; Multi-manned; Resource-constrained; Salp swarm algorithm; Salp swarm algorithm; Epsilon constraint; Resource-constrained; Line balancing; Multi-manned; Mixed-model
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.109759

Due to the current popularity of product customization and unsuitability of single-person disassembly lines for large-size products, as well as considering the needs for supporting resources (machines/tools), this paper investigates the resource-constraint mixed-model multi-manned disassembly line balancing problem (RCMMDLBP), which needs to achieve task-worker-workstation assignment and sequencing under the constraints of AND/OR precedence relationship, worker idle, resource availability, and resource quantity limitation at the same time. Besides, the cycle time and the overall number of workers are considered as dual objectives to provide flexible application scenarios for managers. To solve this problem, a mixed-integer programming model is established and the epsilon constraint method is used to obtain the exact solutions for small-scale cases. Simultaneously, due to the NP-hard nature, a multi-objective optimization algorithm called self-adaptive salp swarm algorithm with sine cosine algorithm (SSSASCA) is proposed. The encoding and decoding are specifically designed with repairing and simulated annealing strategies corresponding to the properties of RCMMDLBP. Moreover, Cauchy mutation and Logistic chaotic mapping strategies are introduced to increase the population diversity and help to jump out of local optimum. Finally, computational experiments are performed to show the superiority of the SSSASCA by comparing it with MSSA, NSGAII, and MAOS. The results show that SSSASCA stably achieves better Pareto front solutions in 59/60 RCMMDLBP instances under the four evaluation indexes of NS, DPO, IGD, and HV. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method. •Studies a resource constrained mixed-model multi-manned disassembly line balancing problem.•Besides the resource quantity constraints, the resource availability constraint is also considered.•Considers the cycle time and the overall number of workers as dual objectives.•Proposes a self-adaptive salp swarm algorithm with sine cosine algorithm.•Provides specific encoding and decoding mechanisms and involves several helpful operators.