Optimisation method for semiconductor wafer manufacturing system scheduling: Reinforcement learning with decision graph guiding

Abstract

Semiconductor wafer fabrication, as a large-scale and complex discrete manufacturing system, presents significant challenges in shop floor scheduling due to its scale, uncertainty, and re-entrant processing. Additionally, effectively leveraging historical scheduling decision data remains a challenge, limiting the ability of scheduling algorithms to accurately assess the current system state. To address these issues, this paper proposes a reinforcement learning-based optimisation method guided by decision graphs. First, we introduce a multidimensional heterogeneous disambiguation graph to comprehensively represent the operational state of the wafer manufacturing system. Second, we design a graph neural network to characterise the multidimensional disambiguation graph and learn from historical decision-making experiences. Finally, we propose a decision graph-guided action strategy that optimises the reinforcement learning policy, improving training efficiency and the accuracy of action selection. Experimental results demonstrate that our method achieves superior generalisation performance and outperforms traditional approaches. This study provides an effective solution for optimising scheduling in semiconductor wafer manufacturing systems.