An Iterated Greedy Algorithm With Reinforcement Learning for Distributed Hybrid Flowshop Problems With Job Merging

Abstract

The distributed hybrid flowshop scheduling problems (DHFSPs) widely exist in various industrial production processes, and thus have received widespread attention. However, the existing research mainly focuses on interfactory and intermachine collaboration, but ignores collaborative processing between jobs. Therefore, this article considers rescheduling DHFSP with job merging and reworking (DHFRPJM) and establishes a mixed-integer linear programming model. The objective is to minimize the makespan. Based on problem-specific knowledge, a decoding heuristic and initialization strategy considering job merging are designed. An acceleration strategy based on critical path is adopted to save the computational effort of the iterated greedy algorithm. A local search strategy based on a deep reinforcement learning algorithm further improves the performance of the algorithm. Experimental results based on actual production data show that the proposed algorithm outperforms other algorithms in closely related literature.