{
  "abstract": "Short-horizon emergency re-optimization of airport apron ground-support operations works when its objective carries timing stability, and backfires when it does not. Using a new benchmark—89 days of Amsterdam and Chicago ADS-B ground traces including transponder-equipped ramp service vehicles, with semi-synthetic task and fault layers: 3540 counterfactual-controlled fault episodes and 740 test reschedule instances with CP-SAT references (85% proved optimal)—we score every recovery over the 30–120 min emergency horizon using a full-day shadow replay against a matched no-fault counterfactual, with date-level paired inference. CP-SAT with a start-time stability term, or with a lexicographic rule that returns the most plan-preserving plan attaining the first-stage window objective value, matches plan-preserving repair on realized disruption on vehicle-removal faults (median 1.6–2.4 vs. 2.7 ks) and lowers absolute realized tardiness on 19/19 test dates. The same model minimizing only in-window weighted tardiness is worse end-to-end than greedy earliest-due-date dispatch on 19/19 dates (exact p=3.8×10−6). For instances certified as optimal at every churn budget, the unconstrained solve reaches the window optimum of the minimal-churn solve yet realizes 2.6 times more disruption; learned re-optimizers trained on the same objective are worse still, and rolling-horizon repetition amplifies the harm. The harm travels through re-timing and re-sequencing rather than reassignment, so an assignment-only churn penalty does not help. The typical instance has no in-window headroom over greedy dispatch (median learning space 0). The ordering holds across ten generator settings and service-time noise. The benchmark will be made publicly available.",
  "abstract_shown": true,
  "affiliations": [
    "College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618311, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tianyu Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ke Tang",
      "orcid": "0000-0003-1263-904X"
    },
    {
      "affs": [
        0
      ],
      "name": "Youyou Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9cvjhsvansyns.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203778",
  "fulltext_url": null,
  "id": "9cvjhsvansyns",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3778",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2202|Air Traffic Management and Optimization"
    ]
  ],
  "openalex": "W7222925603",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/9cvjhsvansyns.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203778",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3778"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations",
  "title_text": "Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/9cvjhsvansyns",
  "venue": "Mathematics",
  "versions": []
}