A Hybrid Algorithm Based on Particle Swarm Optimization and Simulated Annealing for Job Shop Scheduling

In this paper, an effective hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) is proposed for solving the minimum makespan problem of the job shop scheduling problem (JSSP). The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. In the particle swarm system, a novel concept for the distance and velocity of a particle is proposed to pave the way for the job-shop scheduling problem, and then the formulations of the novel PSO algorithm for the JSSP is also presented. In the simulated annealing process, a new solution in the neighborhood of the original one is generated by using a designed lambdasearch. The hybrid algorithm is examined using a set of benchmark instances with various sizes and levels of hardness and compared with other approaches reported in some existing literatures. The computational results validate the effectiveness of the proposed approaches.