Applying multi objective particle swarm optimization algorithm for sequencing and balancing mixed model assembly line problem with setup times between tasks

Abstract

Abstract Mixed-model assembly is a particular set of production lines assembling a family of product models, with similar specifications. Designing paced assembly lines face two primary problems, Balancing and sequencing. The balancing quality is closely associated with the described production sequence. Although these two are problems of one assembly method but they do not take place at the same time, balancing pose a problem during the line designing, whereas sequencing becomes problematic at fluctuating demand of market. In the present research, we have presented a balancing and sequencing problem and proper times to setup the machines between tasks. Unlike a majority of published studies, this paper contains two successive tasks’ setup times in dynamic periods, in which periods also impact the flowing period. A mathematical description with a number of objective functions containing: reducing the inappropriate assembly lines sequence, reducing setup cost, and reducing the inappropriate products balance and the impact of this situation on incomplete tasks. This problem has a combinatorial nature, therefore, the exact techniques, for example combined integer linear programming cannot solve large-sized problems. Thus, the literature have presented several metaheuristic algorithms to solve the problems nearly optimal. This study uses multi-objective particle swarm optimization algorithm, a suitable approach, to create models and solutions. Various problems are designed in different sizes and compared, the decision variables sensitivity is investigated to prepare managerial intuitions. The findings propose that presented algorithm can solve the research problems more efficiently.