Heuristic Scheduling Method for FMSs Based on P-timed Petri Nets and Deep Learning

Abstract

As for the scheduling issue of flexible manufacturing systems (FMS), a heuristic method is proposed based on Petri nets and deep learning. First, an algorithm is presented to generate a heuristic data set by means of the operation rules of P-timed Petri nets. Second, a deep neural network (DNN) is designed to learn the heuristics of Petri net behavior from the data set. Third, the DNN is used as a heuristic function in a dynamic window search (DWS) algorithm to obtain an optimal or near-optimal schedule strategy for an FMS. Finally, a mechanical arm handling system is taken as an example, and numerical experiments are carried out. The results show that the DNN can represent a heuristic function with high precision, and its average estimation error is less than 0.05%, and that the proposed DWS algorithm is very efficient to resolve a given FMS schedul issue.