The job shop scheduling problem (JSSP) remains a significant hurdle in optimizing production processes. This challenge involves efficiently allocating jobs to a limited number of machines while minimizing factors like total processing time or job delays. While recent advancements in artificial intelligence have yielded promising solutions, such as reinforcement learning and graph neural networks, this paper explores the potential of Large Language Models (LLMs) for JSSP. We introduce the very first supervised 120k dataset specifically designed to train LLMs for JSSP. Surprisingly, our findings demonstrate that LLM-based scheduling can achieve performance comparable to other neural approaches. Furthermore, we propose a sampling method that enhances the effectiveness of LLMs in tackling JSSP.

翻译：作业车间调度问题（JSSP）始终是优化生产流程的重大挑战。该问题涉及在有限机器资源下高效分配作业任务，同时最小化总处理时间或作业延迟等指标。尽管人工智能领域的最新进展（如强化学习和图神经网络）已提出有前景的解决方案，本文探索了大型语言模型（LLMs）解决JSSP的潜力。我们首次构建了专门用于训练LLMs解决JSSP的12万条监督数据集。令人惊讶的是，研究结果表明基于LLM的调度方法能达到与其他神经方法相当的性能。此外，我们提出了一种采样方法，可显著提升LLMs处理JSSP问题的效能。