An Improved Particle Swarm Optimization-Based Approach for Production Scheduling Problems

Abstract

Job-shop scheduling problem (JSSP) is very common in a discrete manufacturing environment. It deals with multi-operation models, which are different from the flow shop models. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on particle swarm optimization (PSO) and simulated annealing (SA) technique is presented. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, computer simulations have shown that the proposed hybrid approach is of high speed and efficiency