LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

Abstract: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.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2607.03651 [cs.LG]

(or arXiv:2607.03651v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.03651 Focus to learn more arXiv-issued DOI via DataCite

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