Railway node data.
<div><p>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 algo...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852014708715945984 |
|---|---|
| author | Dandan Wang (286632) |
| author2 | Ni Sun (11936027) |
| author2_role | author |
| author_facet | Dandan Wang (286632) Ni Sun (11936027) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dandan Wang (286632) Ni Sun (11936027) |
| dc.date.none.fl_str_mv | 2025-11-19T18:32:12Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332537.g004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Railway_node_data_/30658992 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Biological Sciences not elsewhere classified cost drops 28 convergence time overruns co &# 8322 capacity matching degree time optimization paradigm sudden demand changes second equilibrium convergence scale logistics networks reinforcement learning methods node load imbalance combines sparse gradient traditional methods objective optimization node network global logistics demand balance accelerated equilibrium tensor decomposition study proposes scalable real research innovates realizes sub prediction error peak periods framework achieves falls 27 existing algorithms driven framework disturbance recovery constrained multi changing supply benchmark performance 4 %, |
| dc.title.none.fl_str_mv | Railway node data. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_b0d2cdf7ce02bfd9f5e2c261a0e01a94 |
| identifier_str_mv | 10.1371/journal.pone.0332537.g004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30658992 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Railway node data.Dandan Wang (286632)Ni Sun (11936027)EcologyScience PolicyBiological Sciences not elsewhere classifiedcost drops 28convergence time overrunsco &# 8322capacity matching degreetime optimization paradigmsudden demand changessecond equilibrium convergencescale logistics networksreinforcement learning methodsnode load imbalancecombines sparse gradienttraditional methodsobjective optimizationnode networkglobal logisticsdemand balanceaccelerated equilibriumtensor decompositionstudy proposesscalable realresearch innovatesrealizes subprediction errorpeak periodsframework achievesfalls 27existing algorithmsdriven frameworkdisturbance recoveryconstrained multichanging supplybenchmark performance4 %,<div><p>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.</p></div>2025-11-19T18:32:12ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332537.g004https://figshare.com/articles/figure/Railway_node_data_/30658992CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306589922025-11-19T18:32:12Z |
| spellingShingle | Railway node data. Dandan Wang (286632) Ecology Science Policy Biological Sciences not elsewhere classified cost drops 28 convergence time overruns co &# 8322 capacity matching degree time optimization paradigm sudden demand changes second equilibrium convergence scale logistics networks reinforcement learning methods node load imbalance combines sparse gradient traditional methods objective optimization node network global logistics demand balance accelerated equilibrium tensor decomposition study proposes scalable real research innovates realizes sub prediction error peak periods framework achieves falls 27 existing algorithms driven framework disturbance recovery constrained multi changing supply benchmark performance 4 %, |
| status_str | publishedVersion |
| title | Railway node data. |
| title_full | Railway node data. |
| title_fullStr | Railway node data. |
| title_full_unstemmed | Railway node data. |
| title_short | Railway node data. |
| title_sort | Railway node data. |
| topic | Ecology Science Policy Biological Sciences not elsewhere classified cost drops 28 convergence time overruns co &# 8322 capacity matching degree time optimization paradigm sudden demand changes second equilibrium convergence scale logistics networks reinforcement learning methods node load imbalance combines sparse gradient traditional methods objective optimization node network global logistics demand balance accelerated equilibrium tensor decomposition study proposes scalable real research innovates realizes sub prediction error peak periods framework achieves falls 27 existing algorithms driven framework disturbance recovery constrained multi changing supply benchmark performance 4 %, |