Disassembly Line Balancing by Using Simulation Optimization

Authors : Muhammet Enes AKPINAR, Mehmet Ali ILGIN, Hüseyin AKTAŞ

Pages : 63-84

Doi:10.17093/alphanumeric.891406

View : 24 | Download : 14

Publication Date : 2021-06-30

Article Type : Research Paper

Abstract :Increasing environmental awareness in today`s society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life insert ignore into journalissuearticles values(EOL); products through different options insert ignore into journalissuearticles values(e.g., recycling, remanufacturing,);. Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics insert ignore into journalissuearticles values(i.e., genetic algorithms insert ignore into journalissuearticles values(GAs); and simulated annealing insert ignore into journalissuearticles values(SA);); are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.
Keywords : Line Balancing, Disassembly, Genetic Algorithms, Simulated Annealing, Simulation