Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling
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| Veröffentlicht in: | Machines vol. 13, no. 5 (2025), p. 361 |
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| 035 | |a 3212071253 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231531 |2 nlm | ||
| 100 | 1 | |a Zeng Yuhang |u School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; zengyuhang@whut.edu.cn (Y.Z.); louping@whut.edu.cn (P.L.); 305838@whut.edu.cn (C.F.); quanliu@whut.edu.cn (Q.L.); hujiwei@whut.edu.cn (J.H.) | |
| 245 | 1 | |a Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Language | ||
| 653 | |a Integer programming | ||
| 653 | |a Deep learning | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Feedback | ||
| 653 | |a Optimization | ||
| 653 | |a Job shops | ||
| 653 | |a Prompt engineering | ||
| 653 | |a Automation | ||
| 653 | |a Manufacturing | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Heuristic | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Representations | ||
| 653 | |a Natural language | ||
| 653 | |a Mathematical programming | ||
| 653 | |a Scheduling | ||
| 653 | |a Convergence | ||
| 653 | |a Large language models | ||
| 653 | |a Decision making | ||
| 653 | |a Preferences | ||
| 653 | |a Design | ||
| 653 | |a Cost analysis | ||
| 653 | |a Literature reviews | ||
| 653 | |a Algorithms | ||
| 653 | |a Job shop scheduling | ||
| 700 | 1 | |a Lou, Ping |u School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; zengyuhang@whut.edu.cn (Y.Z.); louping@whut.edu.cn (P.L.); 305838@whut.edu.cn (C.F.); quanliu@whut.edu.cn (Q.L.); hujiwei@whut.edu.cn (J.H.) | |
| 700 | 1 | |a Hu, Jianmin |u School of Information Engineering, Hubei University of Economics, Wuhan 430205, China | |
| 700 | 1 | |a Fan Chuannian |u School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; zengyuhang@whut.edu.cn (Y.Z.); louping@whut.edu.cn (P.L.); 305838@whut.edu.cn (C.F.); quanliu@whut.edu.cn (Q.L.); hujiwei@whut.edu.cn (J.H.) | |
| 700 | 1 | |a Liu, Quan |u School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; zengyuhang@whut.edu.cn (Y.Z.); louping@whut.edu.cn (P.L.); 305838@whut.edu.cn (C.F.); quanliu@whut.edu.cn (Q.L.); hujiwei@whut.edu.cn (J.H.) | |
| 700 | 1 | |a Hu, Jiwei |u School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China; zengyuhang@whut.edu.cn (Y.Z.); louping@whut.edu.cn (P.L.); 305838@whut.edu.cn (C.F.); quanliu@whut.edu.cn (Q.L.); hujiwei@whut.edu.cn (J.H.) | |
| 773 | 0 | |t Machines |g vol. 13, no. 5 (2025), p. 361 | |
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