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Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling

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Vydáno v:Machines vol. 13, no. 5 (2025), p. 361
Hlavní autor: Zeng Yuhang
Další autoři: Lou, Ping, Hu, Jianmin, Fan Chuannian, Liu, Quan, Hu, Jiwei
Vydáno:
MDPI AG
Témata:
Language
Integer programming
Deep learning
Combinatorial analysis
Feedback
Optimization
Job shops
Prompt engineering
Automation
Manufacturing
Machine learning
Performance evaluation
Heuristic
Generative artificial intelligence
Representations
Natural language
Mathematical programming
Scheduling
Convergence
Large language models
Decision making
Preferences
Design
Cost analysis
Literature reviews
Algorithms
Job shop scheduling
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Abstrakt:The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. Therefore, a human feedback-based large language model-assisted deep reinforcement learning (HFLLMDRL) framework is proposed to solve this problem, in which few-shot prompt engineering by human feedback is utilized to assist in designing instructive reward functions and guiding policy convergence. Additionally, a self-adaptation symbolic visualization Kolmogorov–Arnold Network (KAN) is integrated as the policy network in DRL to enhance state feature representation, thereby improving learning efficiency. Experimental results demonstrate that the proposed framework significantly boosts both learning performance and policy convergence, presenting a novel approach to the JSSP.
ISSN:2075-1702
DOI:10.3390/machines13050361
Zdroj:Engineering Database