A Novel Particle Swarm Optimization Algorithm for Permutation Flow-Shop Scheduling Problem

Abstract

Obtaining the optimal schedule for permutation flow-shop scheduling problem (PFSP) is very important for manufacturing systems. A lot of approaches have been applied for PFSP to minimize makespan, but current algorithms cannot be solved to guarantee optimality. In this paper, based on Particle Swarm Optimization (PSO), a novel PSO (NPSO) is proposed for PFSP with the objective to minimize the makespan. To make original PSO suitable for discrete problems, some improvements and relative techniques for original PSO, such as, Particle representation based on PPS, different crossover and mutation of genetic algorithm (GA) used to avoid premature. Many classical problems have been used to evaluate the performance of the proposed NPSO. Through several comparisons between NPSO and PSO, we obtain that the NPSO is clearly more efficacious than original PSO for PFSP to minimize makespan.