A multi-factory collaborative iterated greedy algorithm for distributed flowshop scheduling with blocking constraint

Abstract Due to the multiple factory production pattern is becoming increasingly apparent, the distributed permutation flowshop scheduling problem (DPFSP) and its extension are studied. In this study, we consider the no buffers between adjacent machines and the setup time of adjacent jobs in DPFSP, and formed a distributed blocking flowshop scheduling problem with sequence-dependent setup times, called DBFSP_SDST. To better study this problem, we first construct mixed-integer linear programming (MILP) and verify the correctness of MILP by using the Gurobi solver. Then, we proposed a multi-factory collaborative iterated greedy algorithm, called mIG to solve the above-formulated model. In mIG, a rapid initialization strategy is proposed to generate a solution with high quality by using refresh accelerated calculation. Two iterative processes are designed with a certain probability to increase the diversity of solutions. Furthermore, according to the distributed characteristic, cross-factory and factory-inner strategies are proposed in iterative process II, the two strategies cooperate with each other to generate new solutions, which balances the exploration and exploitation of the algorithm. Numerous experiments have been conducted to test the performance of mIG, and the computational results demonstrate that mIG has obvious superiority over the state-of-the-art algorithms for DBFSP_SDST.