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The paper introduces DScheLLM, a dynamic scheduling approach using a fine-tuned Huawei OpenPangu Embedded-7B large language model within a dual-system (fast-slow) reasoning architecture to address disruptions in job shop scheduling. The model is trained on datasets generated from exact schedules obtained via an operations research solver, enabling it to handle dynamic events effectively. Experiments on standard benchmarks demonstrate the fast-thinking mode generates high-quality schedules efficiently, while the slow-thinking mode produces solver-compatible decision inputs.
LLMs can now dynamically reschedule job shop tasks in response to real-time disruptions, outperforming traditional event-specific models.
Production scheduling is highly susceptible to dynamic disruptions, such as variations in processing times, machine availability, and unexpected task insertions. Conventional approaches typically rely on event-specific models and explicit analytical formulations, which limits their adaptability and generalization across previously unseen disturbances. To overcome these limitations, this paper proposes DScheLLM, a dynamic scheduling approach that leverages fine-tuned large language models within a dual-system (fast-slow) reasoning architecture to address disturbances of different scales. A unified large language model-based framework is constructed to handle dynamic events, where training datasets for both fast and slow reasoning modes are generated using exact schedules obtained from an operations research solver. The Huawei OpenPangu Embedded-7B model is subsequently fine-tuned under the hybrid reasoning paradigms using LoRA. Experimental evaluations on standard job shop scheduling benchmarks demonstrate that the fast-thinking mode can efficiently generate high-quality schedules and the slow-thinking mode can produce solver-compatible and well-formatted decision inputs. To the best of our knowledge, this work represents one of the earliest studies applying large language models to job shop scheduling in dynamic environments, highlighting their considerable potential for intelligent and adaptive scheduling optimization.