QIAUJournal of Optimization in Industrial Engineering2251-990492020160925A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers91824610.22094/joie.2016.246ENParvizFattahiBu-Ali Sina UniversityParvanehSamoueiBu-Ali Sina UniversityJournal Article20140902This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the workerâ€™s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm. This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the workerâ€™s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithmhttp://www.qjie.ir/article_246_f4a11b067ffa0ea046885a8261301508.pdf