An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems

Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combining of several optimization criteria induces additional complexity and new problems. Particle swarm optimization is an evolutionary computation technique mimicking the behavior of flying birds and their means of information exchange. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum and has been proved to be effective for a variety of situations, including scheduling and sequencing. By reasonably hybridizing these two methodologies, we develop an easily implemented hybrid approach for the multi-objective flexible job-shop scheduling problem (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.