Abstract
In order to improve the self-learning and adaptive capabilities of smart shop floor, this paper proposes an adaptive scheduling method based on deep Q network (DQN). In this study, a dual network scheduling model with a scheduling experience pool is established to improve the efficiency and stability of the scheduling model convergence. Combined with other various functional modules that cooperate with each other, it completes real-time interaction among smart shop floor data, and realizes online monitoring on the scheduling model completely without supervision. The proposed method is verified on the MiniFab semiconductor production shop floor model, and results have shown that the proposed scheduling method is more adaptable to changes in the production environment than simple scheduling rules, ensuring stable performance.
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