{"ID":2832212,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06351","arxiv_id":"2512.06351","title":"LLM-Upgraded Graph Reinforcement Learning for Carbon-Aware Job Scheduling in Smart Manufacturing","abstract":"This paper presents \\textsc{Luca}, a \\underline{l}arge language model (LLM)-\\underline{u}pgraded graph reinforcement learning framework for \\underline{c}arbon-\\underline{a}ware flexible job shop scheduling. \\textsc{Luca} addresses the challenges of dynamic and sustainable scheduling in smart manufacturing systems by integrating a graph neural network and an LLM, guided by a carefully designed in-house prompting strategy, to produce a fused embedding that captures both structural characteristics and contextual semantics of the latest scheduling state. This expressive embedding is then processed by a deep reinforcement learning policy network, which generates real-time scheduling decisions optimized for both makespan and carbon emission objectives. To support sustainability goals, \\textsc{Luca} incorporates a dual-objective reward function that encourages both energy efficiency and scheduling timeliness. Experimental results on both synthetic and public datasets demonstrate that \\textsc{Luca} consistently outperforms comparison algorithms. For instance, on the synthetic dataset, it achieves an average of 4.1\\% and up to 12.2\\% lower makespan compared to the best-performing comparison algorithm while maintaining the same emission level. On public datasets, additional gains are observed for both makespan and emission. These results demonstrate that \\textsc{Luca} is effective and practical for carbon-aware scheduling in smart manufacturing.","short_abstract":"This paper presents \\textsc{Luca}, a \\underline{l}arge language model (LLM)-\\underline{u}pgraded graph reinforcement learning framework for \\underline{c}arbon-\\underline{a}ware flexible job shop scheduling. \\textsc{Luca} addresses the challenges of dynamic and sustainable scheduling in smart manufacturing systems by in...","url_abs":"https://arxiv.org/abs/2512.06351","url_pdf":"https://arxiv.org/pdf/2512.06351v1","authors":"[\"Zhiying Yang\",\"Fang Liu\",\"Wei Zhang\",\"Xin Lou\",\"Malcolm Yoke Hean Low\",\"Boon Ping Gan\"]","published":"2025-12-06T08:53:30Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CL\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}