A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem

Biogeography-based optimization is a novel evolutionary algorithm which mimics the immigration and emigration of species among habitats. In this paper, the biogeography-based optimization is combined with some heuristics to construct an effective hybrid algorithm for solving the fuzzy flexible job-shop scheduling problem. First, path relinking technique is employed as migration operation to generate a new solution. Then, an insertion-based local search heuristic is introduced and embedded in the biogeography-based optimization to modify the mutation operator. Moreover, an efficient machine assignment rule is also proposed to decode the representation based on the operation sequence. Consequently, the exploration and exploitation abilities of the hybrid algorithm are enhanced and well balanced. Computational results and the comparisons with some existing algorithms are presented to show the effectiveness of the proposed hybrid scheme.