大模型能自动生成媲美专业算法的运筹优化方案。
LLMs Can Design Near-Optimal OR Algorithms
- 用单次无调参提示让大模型直接生成算法，无需人工干预。
- 在库存、排队网络等场景中，性能接近或超越现有最优方法。
- 模型迭代快，新版本表现显著提升，适合算法快速原型设计。
我们探讨大型语言模型（LLMs）能否为明确设定的运筹学（OR）问题设计有效算法。研究涵盖库存控制、排队网络控制和组合优化问题。评估两种使用层级：第一层，模型接收单一问题实例并返回该实例的解；第二层，模型仅接收问题类别描述和参数范围，返回一个将参数映射到解的通用算法。人类输入极少：仅需一个未调优的提示，模型通过固定计算预算的Python沙盒工具执行。最强模型gpt-5.6-sol在几乎所有测试实例中表现达到或超过现有最佳方法。这一结果在第二层依然成立，即算法在未见测试实例前已确定。性能随近八个月内发布的模型版本显著提升，表明该能力发展迅速。因此，对于所研究的明确运筹问题，单次无调参的大模型查询即可生成与专用方法竞争的算法。这些结果表明，前沿大模型可成为明确运筹问题算法设计的可靠实证基准。
原文摘要 · Abstract (English)
We ask whether large language models (LLMs) can design effective algorithms for well-specified operations research (OR) problems. We study inventory control, queueing network control, and assortment optimization. We evaluate two levels of LLM use: at level 1, the model receives one problem instance and returns a solution for that instance; at level 2, it receives only the problem class description and broad parameter ranges, and returns an algorithm that maps instance parameters to solutions. Human input is minimal: we give one untuned prompt that describes the problem, and the model has access to a Python sandbox tool with a fixed compute budget. The strongest model we test, gpt-5.6-sol, matches or outperforms the best existing method on almost all evaluated instances. This holds even at level 2, where the returned algorithm is fixed before seeing the evaluation instances. Performance also improves sharply across models released less than eight months apart, suggesting that this capability is moving quickly. Thus, for the well-specified operations problems we study, a single untuned LLM query can already produce algorithms competitive with specialized methods. These results suggest that frontier LLMs can be a serious empirical baseline for algorithm design in well-specified OR problems.
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