LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

• URL: http://arxiv.org/abs/2607.03651v1
• Date: Sat, 04 Jul 2026 00:26:19 GMT
• Title: LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs
• Abstract summary: We propose a novel framework where a large language model (LLM) agent iteratively proposes hub capacity decisions guided by natural-language business context descriptions.<n>This demonstrates that LLMs can serve as a contextual bridge, integrating qualitative business insights into Operations Research.
• Score: 2.8598706593198924
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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