Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning

Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Het...

詳細記述

書誌詳細

出版年:	Machines
主要な著者:	Hengliang Tang, Jinda Dong
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2024-08-01
主題:	deep reinforcement learning flexible job-shop scheduling problem heterogeneous graph neural network manufacturing job-shop scheduling
オンライン･アクセス:	https://www.mdpi.com/2075-1702/12/8/584