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19 October 2023 Learning to schedule job shop scheduling problem with maintenance time using graph node embedding and deep reinforcement learning
Xiangzhen Fang, Jin Li, Yilei Wang
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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270950 (2023) https://doi.org/10.1117/12.2684742
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
The Priority Dispatching Rules (PDRs) and business solvers are widely employed for solving real-world scheduling problems, such as Job Shop Scheduling (JSSP) and JSSP with maintenance time (JSSP-MT) problems. However, the effective designs of PDRs and mathematical modelings are tedious and complex relying heavily on a myriad of specialized knowledge. In this paper, we propose an approach to automatically learn PDRs based on Graph Node Embedding (GNE) and Deep Reinforcement Learning (DRL). We describe JSSP as a disjunctive graph and utilize a GNE approach to facilitate better state embeddings. Ablation studies demonstrate the significant positive contribution of GNE both on JSSP and JSSP-MT. Experiments show that some high-quality combined PDRs can be learned with better approximate solutions against the traditional single PDRs. Solutions produced by our approach are much closer to those from mathematical solvers than previous methods.
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Xiangzhen Fang, Jin Li, and Yilei Wang "Learning to schedule job shop scheduling problem with maintenance time using graph node embedding and deep reinforcement learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270950 (19 October 2023); https://doi.org/10.1117/12.2684742
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KEYWORDS
Matrices
Computer programming
Mathematical optimization
Industry
Performance modeling
Neural networks
Vector spaces
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