An Algorithm for Solving Flexible Job Shop Scheduling Problems Based on Multi-objective Particle Swarm Optimization

A new method based on multi-objective particle swarm optimization is proposed to deal with the flexible job shop scheduling problems with multiple objectives, minimizing completion time, total machine workload and the biggest machine workload. This algorithm adopts linear weighting method to change multi-objective optimization problem into the single objective optimization problem, and introduces random and uniform design method to produce weight coefficient, which ensures the diversity and uniform distribution of pareto set. Besides, elite reserved strategy and dynamic neighborhood operator are designed to maintain the diversity of population and improve search capabilities of particles. Particle is presented in the form of binary group. In order to solve process scheduling priority issues and machinery distribution, encoding process, consisting of extended operation and priority rule, is designed. Finally, the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the flexible job shop scheduling problems.