A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.