An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem

The BBO algorithm is deeply studied by integrating several novel local search heuristics.A hybrid algorithm called HBBO is proposed for solving the DAPFSP.The performance of the HBBO is evaluated by using 1710 benchmark instances.New best solutions are obtained by the proposed hybrid scheme. Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an effective hybrid biogeography-based optimization (HBBO) algorithm that integrates several novel heuristics is proposed to solve the DAPFSP with the objective of minimizing the makespan. Firstly, the path relinking heuristic is employed in the migration phase as product local search strategy to optimize the assembly sequence. Secondly, an insertion-based heuristic is used in the mutation phase to determine the job permutation for each product. Then, a novel local search method is designed based on the problem characteristics and embedded in the HBBO scheme to further improve the most promising individual. Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found.

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