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.

翻译：本文提出\\textsc{Luca}，一种基于\\underline{l}大语言模型（LLM）\\underline{u}升级的图强化学习框架，用于\\underline{c}碳\\underline{a}感知的柔性作业车间调度。\\textsc{Luca}通过集成图神经网络与LLM，并辅以精心设计的内部提示策略，生成融合嵌入，以捕捉最新调度状态的结构特征与上下文语义，从而应对智能制造系统中动态与可持续调度的挑战。该表达性嵌入随后由深度强化学习策略网络处理，生成针对完工时间和碳排放目标优化的实时调度决策。为支持可持续性目标，\\textsc{Luca}采用双目标奖励函数，同时鼓励能源效率与调度时效性。在合成与公开数据集上的实验结果表明，\\textsc{Luca}始终优于对比算法。例如，在合成数据集上，与性能最佳的对比算法相比，其在保持相同排放水平的同时，平均降低完工时间4.1%，最高可达12.2%。在公开数据集上，完工时间与排放方面均观察到额外增益。这些结果证明\\textsc{Luca}在智能制造中的碳感知调度方面是有效且实用的。