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A two-stage decision-making method for real-time response in manufacturing systems: Decision recommendation and scheduling generation
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DOI：10.1016/j.aei.2026.104782.png)
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Abstract
En 中文
Decision-making is an effective way to resolve production anomalies, restore stable production, and ensure on-time order delivery. However, in actual manufacturing workshops, rescheduling not only increases adjustment costs but also introduces greater uncertainty. Although existing research on real-time and predictive decision-making can improve decision accuracy, balancing decision accuracy with production stability remains challenging. This paper proposes a two-stage decision-making method to enhance the applicability of decision-making techniques in manufacturing workshops. In the first stage, a decision recommendation model is constructed. To reduce data redundancy caused by random walk strategies, a subgraph model of production decision is developed. Then a decision recommendation model based on Finite-order DeepWalk is proposed to address the question of what level of decision-making is required. For production scenarios that recommend rescheduling, the second stage builds a scheduling generation model. Considering the real-time requirements of production decision-making, a deep reinforcement learning model is employed to learn historical decision strategies and enable precise adjustment. Finally, the proposed method is validated using a real manufacturing workshop. In the decision recommendation task, the proposed method achieves the best performance, with Accuracy, Precision, Recall, and F1-score reaching 0.9962, 0.9951, 0.9968, and 0.9959, respectively, outperforming all baseline models. In the scheduling generation task, the proposed method delivers feasible rescheduling plans and effectively limits completion deviations under production anomalies such as equipment failure and processing delay, thereby mitigating the influence of production anomalies on order delivery.
Keywords:
Decision-making
Rescheduling
Production stability
Finite-order DeepWalk
Deep reinforcement learning
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