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

Mixed-model assembly is a particular set of production lines assembling a family of product models with similar specifications. Designing paced assembly lines faces 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, they do not occur simultaneously; balancing poses a problem during the line designing, whereas sequencing becomes problematic at the fluctuating demand of markets. The present research presents a balancing and sequencing problem and the proper times to set up 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 is described with a number of objective functions, reducing the inappropriate assembly line sequence, reducing setup cost, and reducing the inappropriate product balance and the impact of this situation on incomplete tasks. Thus, the literature has presented several metaheuristic algorithms to solve the problems nearly optimally. This study uses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a suitable approach, to create models and solutions. Various problems are designed and compared in different sizes, and the decision variable sensitivity is investigated to prepare managerial intuitions. The findings propose that the presented algorithm can solve the research problems more efficiently.

Keywords:

Balancing , sequencing , Setup time , MOAs