Application Research of Improved Particle Swarm Optimization Algorithm Used on Job Shop Scheduling Problem

Abstract

AbstractAiming at the needs of the job shop to arrange production scheduling reasonably in the actual industry, an improved particle swarm optimization method is proposed to solve the job shop scheduling problem (JSSP). By analyzing the scheduling characteristics of the job shop and according to various resource constraints, a process-based coding and activity scheduling decoding mechanism suitable for particle swarm optimization is designed. Use the proportional mutation strategy and the ring topology structure to improve the traditional particle swarm optimization algorithm, and combine the elite selection learning strategy to retain excellent individual information which accelerate the convergence speed. The improved particle swarm optimization algorithm is used to solve standard instance problems of different scales, and the effectiveness of the algorithm is verified by comparing with other methods.KeywordsParticle swarm optimizationLarge-scale optimizationJob shop scheduling problem