Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling ProblemOA
Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem
Lin Luo;Xuesong Yan;Qinghua Wu;Victor S.Sheng
School of Computer Science,China University of Geosciences,Wuhan 430078,ChinaSchool 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,ChinaFaculty of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan 430205,ChinaDepartment of Computer Science,Texas Tech University,Lubbock,TX 79409-3104,USA
Hybrid Flow-shop Scheduling Problem(HFSP)real-time schedulingDeep Reinforcement Learning(DRL)evolution strategiesintelligent manufacturingmulti-factories
Hybrid Flow-shop Scheduling Problem(HFSP)real-time schedulingDeep Reinforcement Learning(DRL)evolution strategiesintelligent manufacturingmulti-factories
《清华大学学报自然科学版（英文版）》 2026 (1)
125-141,17
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).
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