Investigation of Particle Swarm Optimization for Job Shop Scheduling Problem

Job shop scheduling problem has stronger processing constraints, and it is a kind of well-known combination optimization problem. Particle swarm optimization algorithm is employed to solve the job shop scheduling problem, and the objective is minimizing the maximum completion time of all the jobs. The particle representation based on operation-particle position sequence is proposed. In the particle representation, the mapping between the particle and the scheduling solution is established through connecting the operation sequence of all the jobs with the particle position sequence. According to processing constraints of the problem, each operation in the operation sequence of all the jobs is assigned on each machine in turn to form the scheduling solution. The particle representation can ensure that the scheduling solutions decoded are feasible and can follow the particle swam optimization algorithm model. The computational results show that particle swarm optimization algorithm can effectively solve the job shop scheduling problem.