Hybrid particle swarm optimization approach for multi-objective flexible job-shop scheduling problems

Particle swarm optimization (PSO) is discussed, which combines local search and global search, (possessing) high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum. By reasonably hybridizing these two methodologies, an easily (implemented) (hybrid) algorithm for the multi-objective (Flexible) job-shop scheduling problem (FJSP) is presented. The computational results show that the proposed algorithm is a viable and effective approach for the multi-objective FJSP.