{
  "abstract": "High-frequency supply-chain disruptions require decision methods that combine many-objective search with policy-text constraints, yet hybrid systems are often described without sufficiently precise mathematical and evidentiary boundaries. This study presents a classical-computing Quantum-Inspired NSGA-III-BERT decision engine with three separable components: probabilistic population search, policy-aware dynamic reference-point adjustment, and an edge-cloud deployment layer. The robust formulation fixes binary route-activation decisions in the first stage and restricts affine recourse to continuous flow, inventory, and reserve-capacity variables. Across the reported 30-run summaries, the proposed method obtained HV = 0.947 with SD = 0.012 and IGD = 0.082 with SD = 0.007, together with cost and carbon reductions of 19.7% (SD = 0.7%) and 24.5% (SD = 0.9%), respectively. Because the archived package does not preserve paired run-level outputs, inferential comparisons are reported as summary-statistic Welch tests with Holm correction rather than as reconstructed paired tests. Pilot-enterprise observations are interpreted as descriptive deployment evidence rather than causal estimates. The study therefore contributes an explicit algorithm, a mathematically consistent recourse model, and an evidence-bounded interpretation of the empirical results.",
  "abstract_shown": true,
  "affiliations": [
    "College of Economics and Management, Hefei Normal University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wei Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fp9dwt6vw7e8s.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-68917-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-68917-5",
  "id": "fp9dwt6vw7e8s",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-68917-5",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1408|Supply Chain Resilience and Risk Management"
    ]
  ],
  "openalex": "W7204634202",
  "pdf_url": "https://www.nature.com/articles/s41598-026-68917-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/fp9dwt6vw7e8s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:46Z",
      "native_id": "8287ab78d0224a22bfb4ff8477c99566",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-68917-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Quantum-inspired NSGA-III-BERT hybrid decision engine for high-frequency supply chain resilience configuration: design, simulation and engineering framework",
  "title_text": "Quantum-inspired NSGA-III-BERT hybrid decision engine for high-frequency supply chain resilience configuration: design, simulation and engineering framework",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:46Z",
  "url": "https://research.draconitic.com/paper/fp9dwt6vw7e8s",
  "venue": "Scientific Reports",
  "versions": []
}