基于文本业务输入的大语言模型引导的交通枢纽容量规划

中文摘要 AI 辅助

传统枢纽容量规划模型虽能有效优化定量输入，但常无法处理定性业务背景。我们提出一个新颖框架，其中大语言模型（LLM）代理根据自然语言业务背景描述迭代地提出枢纽容量决策。关键机制是思维链推理协议：LLM构建一个结构化决策表，根据变化的隐含方向和幅度将每个上下文项目映射到特定的容量调整。然后通过与优化模型的反馈回路验证新的容量决策，该优化模型提供基于路由的性能指标以指导代理的选择。在美国东南部一个真实的13枢纽货运网络上，相对于隐藏的真实情况，我们的框架实现了2.8%的最优差距，与没有文本业务输入的传统优化模型产生的11.0%的差距相比有显著改善。这表明大语言模型可以作为一个上下文桥梁，将定性业务见解整合到运筹学工作流程中。

英文摘要

While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context. We propose a novel framework where a large language model (LLM) agent iteratively proposes hub capacity decisions guided by natural-language business context descriptions. The key mechanism is a chain-of-thought reasoning protocol: the LLM constructs a structured decision table that maps each contextual item to specific capacity adjustments based on the implied direction and magnitude of changes. The new capacity decision is then validated through a feedback loop with an optimization model, which provides routing-based performance metrics to guide the agent's selection. On a real-world 13-hub freight network in the southeastern US, our framework achieves a 2.8% optimality gap relative to the hidden ground-truth, a significant improvement over the 11.0% gap produced by the traditional optimization model without textual business inputs. This demonstrates that LLMs can serve as a contextual bridge, integrating qualitative business insights into Operations Research workflows.

发表机构

• Georgia Institute of Technology（佐治亚理工学院）

机构由 AI 辅助整理，请以论文原文为准。