Abstract: In the actual FJSP(Flexible Job-shop Scheduling Problem) production environment, there is not only the processing time of the workpiece, but also the transport time of the workpiece between the machines, so the flexible job shop scheduling considering the transport time is more practically significant. A hybrid discrete particle swarm optimization algorithm is proposed to solve the flexible job-shop scheduling problem considering transport time. Aiming at the instability and precocity of particle swarm optimization algorithm, the neighborhood search algorithm is applied to improve its stability, and the combination of competitive learning mechanism and random restart algorithm are introduced to avoid the precocity of pso algorithm. By comparing recent similar algorithms, the feasibility and effectiveness of the hybrid discrete particle swarm optimization algorithm for FJSP with transport time is proved.

Key words: hybrid discrete particle swarm optimization, flexible job shop scheduling, neighborhood search algorithm, competitive learning mechanism