Solving Multi-Objective Flexible Job Shop Scheduling Problems Using Immune Algorithm

Scheduling for the flexible job-shops has great importance 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 methods because of the high computational complexity. Considering several optimization criteria in this problem will bring on additional complexity and new problems. So it makes traditional methods not practical, and urges new ways of optimization like meta-heuristic algorithms. Immune algorithm is an evolutionary computation technique imitating the behavior of biological immune systems in body. We developed an easily implemented approach for the multiobjective flexible job-shop scheduling problems (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.