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MetaGradient driven strategy decomposition for accelerated equilibrium in large scale logistics networks.

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  • Author(s): Wang D;Wang D; Sun N; Sun N
  • Source:
    PloS one [PLoS One] 2025 Nov 19; Vol. 20 (11), pp. e0332537. Date of Electronic Publication: 2025 Nov 19 (Print Publication: 2025).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Models, Theoretical*; Algorithms
    • Abstract:
      Competing Interests: The authors have declared that no competing interests exist.
      Static models fail to track the fast-changing supply-demand balance in global logistics. For instance, the high-speed rail express corridor exhibits a transport capacity utilisation rate of less than 70% during peak periods, along with a node load imbalance of 0.57. Existing algorithms have been shown to exhibit a 7.8% prediction error and 38% convergence time overruns during sudden demand changes. This study proposes a gradient-driven framework that combines sparse gradient, tensor decomposition, and constrained multi-objective optimization. Cost drops 28.3%, transit time shrinks 37.3%, container turnover rises 41.4%, and CO₂ falls 27.7%. In the 15-node network, the framework achieves a capacity matching degree of 89.3% with a root mean square error of 0.145, which is better than the benchmark performance of traditional methods and reinforcement learning methods. This research innovates a scalable real-time optimization paradigm, realizes sub-second equilibrium convergence and anti-disturbance recovery of large-scale logistics networks, and lays a foundation for intelligent, low-carbon and resilient logistics ecology.
      (Copyright: © 2025 Wang, Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
    • References:
      PLoS One. 2023 Dec 11;18(12):e0295678. (PMID: 38079441)
      Biomimetics (Basel). 2024 Nov 21;9(12):. (PMID: 39727722)
      J Xray Sci Technol. 2025 Sep;33(5):844-865. (PMID: 40350718)
      Sci Rep. 2025 Jul 12;15(1):25195. (PMID: 40652028)
    • Publication Date:
      Date Created: 20251119 Date Completed: 20251119 Latest Revision: 20251123
    • Publication Date:
      20251123
    • Accession Number:
      PMC12629495
    • Accession Number:
      10.1371/journal.pone.0332537
    • Accession Number:
      41259318