Optimizing multi-objective sequencing problem in mixed-model assembly line on just-in-time: particle swarm optimization algorithm

Abstract

This paper deals with the formulation of a sequencing problem with the dual goals of varying the parts utilization at different workstations of the assembly line and varying the workload associated with each workstation; these two objectives are typically inversely correlated with each other, and therefore the simultaneous optimization of both is challenging. Owing to the NP-hardness of the problem, this paper introduces a Discrete Particle Swarm Optimization (DPSO) algorithm, a Memetic Algorithm (MA), a Weighted sum Multi-Objective Genetic Algorithm (MOGAW), and a Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a just-in-time sequencing problem where these objectives are to be optimized simultaneously. The performance of these algorithms is compared against each other on small, medium, and large problems. According to statistical analysis, experimental results show that discrete particle swarm optimization outperforms the other algorithms in respect of a comparison metric.