Solving the permutation flow shop scheduling problem with sequence-dependent setup time via iterative greedy algorithm and imitation learning

Abstract

In recent years, the application of learning-based methods in flow shop scheduling problem has gained considerable attention. However, there are gaps in the quality of their solution due to the difficulty of fully exploring the huge search space faced by learning-based methods and the difficulty of reward function design. In this paper, a hybrid approach of meta-heuristic algorithm and imitation learning (IL) is proposed to solve the permutation flow shop scheduling problem with sequence-dependent setup times (PFSP-SDST). Firstly, jobs are treated as nodes, and the processing time and setup times of PFSP-SDST are considered as features of the nodes, respectively. Secondly, a graph neural network based on an attention feature fusion (AFF) mechanism is designed as an encoder to embed the feature information of the problem. Finally, an iterative greedy algorithm based on critical path is proposed to provide high-quality expert solutions for the IL algorithm. The running results on randomly generated datasets and benchmark datasets demonstrate the effectiveness of the proposed method.