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

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 betwee...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 29; H. 3; S. 589 - 600
Hauptverfasser: Tao, Xin-Rui, Pan, Quan-Ke, Gao, Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2025
Schlagworte:
Collaboration
Distributed hybrid flowshop
Greedy algorithms
Heuristic algorithms
iterated greedy algorithm
job merging
Merging
Optimization
Prediction algorithms
Production facilities
reinforcement learning (RL)
rescheduling
ISSN:1089-778X, 1941-0026
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Zusammenfassung: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2024.3443874