Research on an Adaptive Real-Time Scheduling Method of Dynamic Job-Shop Based on Reinforcement Learning

Abstract

With the rapid development of modern industrialization in our country and the continuous improvement of people’s living standards, the changing market has put forward new requirements for traditional manufacturing enterprises. The order product demand of many manufacturing enterprises is changing from a single variety, large batch to multiple varieties, small batch. In view of this change, the traditional job-shop scheduling method is far from enough, which greatly affects the efficiency of the production job-shop. In order to solve the above problems, this paper proposes a real-time scheduling method based on reinforcement learning applied in the dynamic job-shop and a new type of neural network is designed at the same time. The neural network is designed with the high-dimensional data in the above problem as input, and a policy-based reinforcement learning algorithm is proposed based on this network. In the process of research, it was found that the reinforcement learning method not only enables the agent to use historical data for learning, but also enables it to explore and learn other possible high-reward actions within a certain range, so as to realize the optimization of production goals under real-time scheduling. The effectiveness of the proposed real-time scheduling method is verified by comparing with other common rule-based scheduling methods in the manufacturing environment.