Research on a Real-time Job-shop Scheduling Method Based on Reinforcement Learning

Abstract

At present, manufacturing models are characterized by multi-variety, small batch, and diversification. It is insufficient to use traditional scheduling methods for production management with high performance. A real-time production scheduling system based on reinforcement learning (RL) is suggested in an effort to address the aforementioned issues. A brand-new manufacturing neural network is created to learn the state-action values for production scheduling in real time using high-dimensional data as the input. The detailed setup of network inputs, neural network, action, and reward are also designed. Then, a policy-based reinforcement learning algorithm is proposed to achieve the optimum objective. Finally, By contrasting the proposed scheduling strategy with rule-based approaches in a smart manufacturing environment, its efficacy is demonstrated. according to experimental data, the suggested algorithm can successfully improve performance in the dynamic job-shop environment.