An improved iterated greedy algorithm for the distributed flow shop scheduling problem with sequence-dependent setup times

Abstract

In various flow shop scheduling problems, it is very common that a large-scale production is done. Under this situation, more factories are of more practical interest than a factory. Thus, the distributed permutation flow shop scheduling problems (DPFSPs) have been attracted attentions by researchers. However, the DPFSP is more complicated than the traditional flow shop scheduling problems. It considers not only the processing order of the jobs, but also how to distribute the jobs to multiple factories for parallel processing. In addition, the sequence- dependent setup time (SDST) constraint of machines is taken into account to well study the above DPFSP with SDST. This paper presents a simple and effective iterated greedy algorithm. It is proposed to replace the traditional insertion-based local search with exchange-based local search, which greatly improves the search efficiency. The proposed new iterated greedy (NIG) algorithm is applied to test instances, and compares with the state- of-the-art algorithms. Our empirical results demonstrate that the proposed algorithm outperforms the compared algorithms and can obtain the best solution of DPFSP.