Study and Application of an Improved Particle Swarm Optimization in Job-Shop Scheduling Problem

The job-shop scheduling problem (JSP) is a classical NP-hard. The traditional methods solving it have their own Advantages and shortcomings. The powerful information processing capabilities of immune system provides people enlightenment for its artificial application. As a result, immune algorithm has emerged, and gradually been applied to many engineering practices. Due to the stubborn nature of the JSP, a new method based on immune particle swarm algorithm (IPA) is initially brought forward to solve job-shop scheduling problem. In this method, the IPA flow structure is presented via combining the immune theory and the particle swarm algorithm. The encoding method based on operation is used by IPA. And operator is designed according to vaccination, variation and immune selection. Finally, the simulation result shows that the IPA has good performance in job-shop scheduling problem.