Scheduling Method of Manufacturing System Based on Colored Petri Nets and Graph Neural Network

When using timed Petri nets for modeling, Petri net model often tends to be huge, and this makes it difficult to design neural networks of Petri nets. To address this issue, this paper proposes a resource scheduling system automated guided vehicle(AGV) path planning method based on colored Petri nets and graph neural networks. First, the AGV system is described as a path Petri net, and different task types are assigned to the Petri net in different colors based on different process requirements, so that the same place or transition in the Petri net can represent multiple meanings, greatly simplifying the structure of the Petri net. Secondly, using the topological structure of the Petri net, a graph neural network that can be used on colored Petri nets is designed. The topological structure of the Petri net is divided into four categories, and is applied to the graph neural network as a correlation matrix for feature aggregation, achieving higher accuracy with fewer parameters. Thirdly, deep reinforcement learning is used to train the graph neural network, and the trained model is used as a heuristic to construct the A* algorithm to solve the scheduling problem of the system. Finally, several numerical experiments are conducted, and the results show that the scheduling method based on colored Petri nets and graph neural networks can simplify the structure of the Petri net while solving the joint problem of AGV task allocation and path planning in a limited time.