Deep Reinforcement Learning Based on Graph Neural Networks for Job-shop Scheduling

Kuo-Hao Ho, Ji-Han Wu, Fan Chiang, Yuan-Yu Wu, Sheng-I Chen, Ted Kuo, Feng-Jian Wang, I-Chen Wu

Abstract: Recently, deep reinforcement learning (DRL) methods attract much attention for solving job-shop scheduling problem (JSP), a NP-hard optimization problem. One of DRL methods is based on priority dispatching rules (PDRs), which is easy to be implemented, to dispatch operations to machines. In this paper, we propose a graph neural network (GNN) to enhance Luo's method [1] to choose a PDR to dispatch. With GNN, our method, trained with small JSP problems, also performs well in large JSP problems. Our experiments show that our method outperforms PDR methods and most of other DRL methods, particularly for large JSP problems.