LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

Excerpt

arXiv:2607.03651v3 Announce Type: replace 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 conte