用13万数据集训练LLM解决车间调度，性能超传统方法15%。
Starjob: Dataset for LLM-Driven Job Shop Scheduling
- 构建首个面向车间调度的监督数据集Starjob，含13万实例
- 基于LLaMA-8B微调后，在DMU和Taillard基准上分别提升15.36%和7.85%
- 首次证明大模型在组合优化中潜力，适合算法研究者参考
大型语言模型（LLMs）在多个领域展现出卓越能力，但其在组合优化问题中的应用仍待探索。本文研究了LLMs在经典组合优化问题——车间调度问题（JSSP）中的适用性，目标是高效分配作业到机器以最小化完工时间。为此，我们提出了Starjob，首个专为训练LLMs设计的监督数据集，包含13万条精心构造的实例。利用该数据集，我们采用LoRA方法对8B参数、4比特量化版LLaMA模型进行微调，构建端到端调度方法。在标准基准上的评估表明，所提方法不仅优于传统优先调度规则（PDRs），还在DMU和Taillard基准上分别实现15.36%和7.85%的显著改进，超越当前先进神经方法如L2D。结果凸显了LLMs在组合优化中的巨大潜力，为该领域未来发展开辟新路径。
原文摘要 · Abstract (English)
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.
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