🤖 AI Summary
This work addresses the dynamic, sustainable, carbon-aware flexible job shop scheduling problem in smart manufacturing. We propose a graph reinforcement learning framework that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs). Through customized prompt engineering, the topological structure and semantic information of scheduling states are jointly encoded into high-quality embeddings; a multi-objective reward function guides deep reinforcement learning to jointly optimize makespan and carbon emissions. To our knowledge, this is the first study to incorporate LLMs into graph-based reinforcement learning for carbon-aware job shop scheduling, endowing the agent with intrinsic semantic understanding. Evaluated on both synthetic and public benchmark datasets, our method achieves an average 4.1% reduction in makespan (up to 12.2%) while significantly lowering carbon emissions, outperforming state-of-the-art algorithms in overall performance.
📝 Abstract
This paper presents extsc{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. extsc{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, extsc{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 extsc{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 extsc{Luca} is effective and practical for carbon-aware scheduling in smart manufacturing.