Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplored. In this paper, we investigate the applicability of LLMs to the Job Shop Scheduling Problem (JSSP), a classic challenge in combinatorial optimization that requires efficient job allocation to machines to minimize makespan. To this end, we introduce Starjob, the first supervised dataset for JSSP, comprising 130k instances specifically designed for training LLMs. Leveraging this dataset, we fine-tune the LLaMA 8B 4-bit quantized model with the LoRA method to develop an end-to-end scheduling approach. Our evaluation on standard benchmarks demonstrates that the proposed LLM-based method not only surpasses traditional Priority Dispatching Rules (PDRs) but also achieves notable improvements over state-of-the-art neural approaches like L2D, with an average improvement of 15.36% on DMU and 7.85% on Taillard benchmarks. These results highlight the untapped potential of LLMs in tackling combinatorial optimization problems, paving the way for future advancements in this area.

翻译：大语言模型（LLMs）已在多个领域展现出卓越能力，但其在组合优化问题求解方面的潜力尚未得到充分探索。本文研究LLMs在作业车间调度问题（JSSP）中的适用性——该问题是组合优化领域的经典挑战，需要将作业高效分配至机器以最小化完工时间。为此，我们提出了首个面向JSSP的监督数据集Starjob，包含13万个专门为训练LLMs设计的调度实例。基于该数据集，我们采用LoRA方法对LLaMA 8B 4位量化模型进行微调，开发出端到端调度方法。在标准基准测试上的评估表明，所提出的基于LLM的方法不仅超越了传统优先级调度规则（PDRs），相较L2D等先进神经方法也取得显著提升：在DMU基准上平均提升15.36%，在Taillard基准上平均提升7.85%。这些成果揭示了LLMs在解决组合优化问题中尚未开发的潜力，为该领域的未来发展开辟了新路径。