LLM-Upgraded Graph Reinforcement Learning for Carbon-Aware Job Scheduling in Smart Manufacturing

This paper presents textscLuca, a underlinelarge language model (LLM)-underlineupgraded graph reinforcement learning framework for underlinecarbon-underlineaware flexible job shop scheduling. textscLuca 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, textscLuca incorporates a dual-objective reward function that encourages both energy efficiency and scheduling timeliness. Experimental results on both synthetic and public datasets demonstrate that textscLuca 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 textscLuca is effective and practical for carbon-aware scheduling in smart manufacturing.