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*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面805-806
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態Published - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
持續時間: 17 7月 202319 7月 2023

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區Taiwan
城市Pingtung
期間17/07/2319/07/23

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