A novel particle swarm optimization (PSO)-based algorithm is developed for job-shop scheduling problems (JSSP), which are the most general and difficult issues in traditional scheduling problems. Our goal is to develop an efficient algorithm based on swarm intelligence for the JSSP. Thereafter a novel concept for the distance and velocity of particles in the PSO is proposed and introduced to pave the way for the JSSP. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing systems, with simulation results showing the possibilities of high quality solutions for typical benchmark problems.
A Novel Job-Shop Scheduling Strategy Based on Particle Swarm Optimization and Neural Network