@article{Luo2026, 
author = {Lin Luo and Xuesong Yan and Qinghua Wu and Victor S. Sheng},
title = {Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {1},
pages = {125-141},
keywords = {Hybrid Flow-shop Scheduling Problem (HFSP), real-time scheduling, Deep Reinforcement Learning (DRL), evolution strategies, intelligent manufacturing, multi-factories},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010141},
doi = {10.26599/TST.2024.9010141},
abstract = {Flexible manufacturing faces the challenge of increasing productivity and conserving resources, especially in complex production environments with dynamic event. This paper addresses a dynamic Hybrid Flow-shop Scheduling Problem (HFSP) with unrelated parallel machines using a Deep Reinforcement Learning (DRL) approach to intelligently allocate continuous new job arrivals while minimizing the total weighted tardiness cost. In this paper, Evolution Strategies-guided Deep Reinforcement Learning (ES-DRL) scheduling model is proposed by designing appropriate state features, scheduling actions, and training strategies. In addition, goal-directed composite rules are proposed to provide effective scheduling actions. Meanwhile, the state transition in the environment is adjusted by introducing key state. The ES-DRL model is then trained to make decisions, indicating the reasoning behind the system design. Experimental results show that ES-DRL outperforms the other comparison algorithms regarding significance. In addition, the experiments are extended to the multi-factories system to further validate the scalability and adaptability of the scheduling model, and this extension also yields encouraging results. These results affirm the universal applicability of ES-DRL for dynamic HFSP.}
}