Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches

• Published in: Annals of operations research 2022-12, Vol.319 (2), p.1793-1843
• Main Authors: Yilmaz, Ömer Faruk, Yazici, Büsra
• Format: Article
• Language: English
• Subjects: Algorithms; Assembly-line balancing; Balancing; Business and Management; Combinatorics; Cycle time; Dismantling; Genetic algorithms; Heterogeneity; Literature reviews; Mathematical optimization; Methods; Multiple objective analysis; Operations research; Operations Research/Decision Theory; Optimization; Optimization models; Original Research; Pareto optimization; Pareto optimum; Performance measurement; Remanufacturing; Sorting algorithms; Theory of Computation; Work stations; Workers; Workloads
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-020-03902-3

The disassembly line plays a vital role to recover the products for remanufacturing enterprises. For this reason, designing and balancing of the disassembly line are important to utilize the economic and tactical benefits. This study explores a multi-objective disassembly line balancing problem (MODLBP) from a different point of view by considering the workers’ heterogeneity and the multi-manned stations where the group-based worker assignment strategy is implemented. Although the MODLBP has been attracting attention in the last decade, to the best of our knowledge, this is the first study investigating the addressed problem in the current form. To further analyze the problem, first, it is described by focusing on the tactical level strategies and operational level scenarios. Subsequently, a novel multi-objective optimization model is formulated with three objectives, that of minimizing overall cost, cycle time, and workload imbalance. On one hand, the improved augmented ϵ-constrained (AUGMECON2) method is used to obtain the Pareto-optimal solutions for small-sized problems. On the other hand, a set of algorithms based on the non-dominated sorting genetic algorithm-II is implemented to gain managerial insights regarding the strategies and scenarios for large-sized problems. A computational study is conducted based on the generated problems to reveal the prominent differences between strategies in terms of performance metrics. According to the computational results, high-quality solutions are achieved when the group-based assignment strategy is realized. Besides, it is revealed from scenario analysis that the training of workers leads to considerable improvements in the system performance.