On algorithms for JSSP based on hybrid graph neural network

Abstract

The Job-Shop Scheduling Problem (JSSP), one of the traditional scheduling issues in combinatorial optimization problems, has been the subject of much research for many years. In this study, to further take into account the influence of time sequence information on the operation between different processes, we develop a network architecture that combines graph neural networks and long short-term memory networks. We then train a policy network using proximal policy optimization to efficiently extract node features from JSSP disjunctive graphs and automate the learning of Priority Dispatch Rules (PDRs). The experimental results demonstrate that the proposed model for solving the JSSP problem has a high generalization performance and a global performance that is noticeably better than the current best PDRs, offering a useful approach for solving the JSSP problem.