{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.03651",
  "author_total": 2,
  "authors": [
    {
      "name": "Xiaoyue Liu"
    },
    {
      "name": "Zheng Dong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cq68qgt63fvnx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.03651",
  "fulltext_url": null,
  "id": "cq68qgt63fvnx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.03651",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2209|Vehicle Routing Optimization Methods"
    ]
  ],
  "openalex": "W7167644075",
  "pdf_url": "https://arxiv.org/pdf/2607.03651",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cq68qgt63fvnx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:38Z",
      "native_id": "2607.03651",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.03651"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs",
  "title_text": "LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:38Z",
  "url": "https://research.draconitic.com/paper/cq68qgt63fvnx",
  "venue": "arXiv",
  "versions": []
}