Abstract
This paper studies the distributed blocking flow shop scheduling problem (DBFSP) using meta-heuristics. A mixed integer programming model for solving the problem is proposed, and then three versions of the hybrid iterated greedy algorithm (HIG1, HIG2, and HIG3) are developed, combining the advantages of an iterated greedy algorithm with the operators of the variable Tabu list, the constant Tabu list, and the cooling schedule. A benchmark problem set is used to assess empirically the performance of the HIG1, HIG2, and HIG3 algorithms. Computational results show that all the three versions of the proposed algorithm can efficiently and effectively minimize the maximum completion time among all factories of the DBFSP, and HIG1 is the most effective.
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