A Hybrid Biogeography-Based Optimization Algorithm for Distributed Assembly Permutation Flowshop Scheduling Problem

This is the authors' abstract. We don't add key points for this paper.

The distributed assembly permutation flowshop scheduling problem (DAPFSP) has become a significant research topic in recent years. However, most existing studies rely on approximation search algorithms, such as metaheuristic algorithms, to address the high complexity of DAPFSP. To overcome the limitations of instability and poor search capability in these algorithms, this study proposes a hybrid biogeography-based optimization (HBBO) algorithm to solve DAPFSP with the objective of minimizing makespan. Two factory allocation methods are employed to generate initial solutions, reducing the possibility of redundant solutions. A crossover operator is utilized to improve the quality of partial solutions during the global search phase of HBBO. Additionally, four migration rate models are comprehensively tested as alternatives to commonly used models. Two neighborhood search methods are incorporated as the local search strategy for HBBO. The Metropolis acceptance criterion is employed to retain some inferior solutions, thereby enhancing solution diversity and preventing the algorithm from getting trapped in local optima. A series of experiments are conducted, and the computational results demonstrate that HBBO delivers promising results for DAPFSP.