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Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem

Lin Luo Xuesong Yan Qinghua Wu Victor S.Sheng

清华大学学报自然科学版（英文版）2026，Vol.31Issue(1)：125-141,17.

清华大学学报自然科学版（英文版）2026，Vol.31Issue(1)：125-141,17.DOI:10.26599/TST.2024.9010141

Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem

Lin Luo ¹Xuesong Yan ²Qinghua Wu ³Victor S.Sheng⁴

作者信息

1. School of Computer Science,China University of Geosciences,Wuhan 430078,China
2. School of Computer Science,China University of Geosciences,Wuhan 430078,China||Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software,Ministry of Education,Wuhan 430074,China
3. Faculty of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan 430205,China
4. Department of Computer Science,Texas Tech University,Lubbock,TX 79409-3104,USA
折叠

摘要

关键词

Hybrid Flow-shop Scheduling Problem(HFSP)/real-time scheduling/Deep Reinforcement Learning(DRL)/evolution strategies/intelligent manufacturing/multi-factories

Key words

Hybrid Flow-shop Scheduling Problem(HFSP)/real-time scheduling/Deep Reinforcement Learning(DRL)/evolution strategies/intelligent manufacturing/multi-factories

引用本文复制引用

Lin Luo,Xuesong Yan,Qinghua Wu,Victor S.Sheng..Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem[J].清华大学学报自然科学版（英文版）,2026,31(1):125-141,17.

基金项目

This work was supported by the National Key Research and Development Program of China(No.2022YFB4501402),the Key Research and Development Program of Hubei Province(No.2023BAB065),and the National Natural Science Foundation of China(No.62073300). （No.2022YFB4501402）

清华大学学报自然科学版（英文版）

ISSN：1007-0214

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