Pareto frontier solution solving process.
<div><p>The high concentration of hazardous sources in chemical parks, which is prone to cause chain accidents, puts forward the demand for dynamic cooperative optimization of emergency resource scheduling. Aiming at the deficiencies of existing studies in the adaptability of dynamic mul...
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2025
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| _version_ | 1852016131689152512 |
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| author | Yuhang Wang (332111) |
| author2 | Mingguang Zhang (436380) Jun Lu (36049) Yufei Gui (11945954) |
| author2_role | author author author |
| author_facet | Yuhang Wang (332111) Mingguang Zhang (436380) Jun Lu (36049) Yufei Gui (11945954) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yuhang Wang (332111) Mingguang Zhang (436380) Jun Lu (36049) Yufei Gui (11945954) |
| dc.date.none.fl_str_mv | 2025-09-30T17:43:26Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332858.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Pareto_frontier_solution_solving_process_/30249403 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Sociology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified warehouse collaborative supply sensitivity analysis confirms pareto front efficiently loss tolerance thresholds integer planning model holds irreplaceable importance generating 41 sets exhibiting excellent convergence elite reservation strategy optimal solution using mogwo algorithm demonstrate key driver determining ii performs superiorly dynamic cooperative optimization comprehensive weighted losses cause chain accidents independent optimization objective chemical industrial park emergency resource scheduling integrates time efficiency demand satisfaction rates resource allocation fairness ii algorithm chemical accidents resource demand weighted method transportation time search efficiency key metrics emergency dispatching conducted using allocation fairness objective mixed objective balance chemical parks dynamic multi xlink "> standard deviation significant increases puts forward paper constructs often accompanied innovatively quantified high concentration hazardous sources hazard scenarios feasible solutions existing studies demand coverage composite score |
| dc.title.none.fl_str_mv | Pareto frontier solution solving process. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The high concentration of hazardous sources in chemical parks, which is prone to cause chain accidents, puts forward the demand for dynamic cooperative optimization of emergency resource scheduling. Aiming at the deficiencies of existing studies in the adaptability of dynamic multi-hazard scenarios and the quantification of resource allocation fairness, this paper constructs a three-objective mixed-integer planning model that integrates time efficiency, demand coverage and allocation fairness. Fairness is innovatively quantified as an independent optimization objective, and a standard deviation-based dynamic resource allocation balance index is proposed, which combines multi-warehouse collaborative supply and multi-resource coupling constraint mechanism to systematically solve the problem of trade-offs between timeliness, adequacy and fairness in emergency dispatching in chemical accidents. The improved NSGA-II algorithm is used to solve the Pareto front efficiently, and the search efficiency is improved by the elite reservation strategy and the congestion adaptive adjustment mechanism. In the case study, comparative experiments with the weighted method and the MOGWO algorithm demonstrate that NSGA-II performs superiorly in key metrics, exhibiting excellent convergence, diversity, and stability. Based on this, a case study is conducted using a chemical industrial park in China as an example, generating 41 sets of weights covering extreme preferences, two-objective balance, and three-objective balance. Decision-makers screen solutions based on loss tolerance thresholds and select the optimal solution using a composite score of comprehensive weighted losses. The study further reveals that improvements in demand satisfaction rates are often accompanied by significant increases in transportation time, while pursuing optimal fairness may weaken overall demand satisfaction levels. Sensitivity analysis confirms that resource demand is the key driver determining the number of feasible solutions, while fairness, as an independent optimization objective, holds irreplaceable importance in emergency scheduling decisions.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2e5c72d2cd8fcef9243afe03bc03f7d7 |
| identifier_str_mv | 10.1371/journal.pone.0332858.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30249403 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Pareto frontier solution solving process.Yuhang Wang (332111)Mingguang Zhang (436380)Jun Lu (36049)Yufei Gui (11945954)EcologySociologyScience PolicyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedwarehouse collaborative supplysensitivity analysis confirmspareto front efficientlyloss tolerance thresholdsinteger planning modelholds irreplaceable importancegenerating 41 setsexhibiting excellent convergenceelite reservation strategyoptimal solution usingmogwo algorithm demonstratekey driver determiningii performs superiorlydynamic cooperative optimizationcomprehensive weighted lossescause chain accidentsindependent optimization objectivechemical industrial parkemergency resource schedulingintegrates time efficiencydemand satisfaction ratesresource allocation fairnessii algorithmchemical accidentsresource demandweighted methodtransportation timesearch efficiencykey metricsemergency dispatchingconducted usingallocation fairnessobjective mixedobjective balancechemical parksdynamic multixlink ">standard deviationsignificant increasesputs forwardpaper constructsoften accompaniedinnovatively quantifiedhigh concentrationhazardous sourceshazard scenariosfeasible solutionsexisting studiesdemand coveragecomposite score<div><p>The high concentration of hazardous sources in chemical parks, which is prone to cause chain accidents, puts forward the demand for dynamic cooperative optimization of emergency resource scheduling. Aiming at the deficiencies of existing studies in the adaptability of dynamic multi-hazard scenarios and the quantification of resource allocation fairness, this paper constructs a three-objective mixed-integer planning model that integrates time efficiency, demand coverage and allocation fairness. Fairness is innovatively quantified as an independent optimization objective, and a standard deviation-based dynamic resource allocation balance index is proposed, which combines multi-warehouse collaborative supply and multi-resource coupling constraint mechanism to systematically solve the problem of trade-offs between timeliness, adequacy and fairness in emergency dispatching in chemical accidents. The improved NSGA-II algorithm is used to solve the Pareto front efficiently, and the search efficiency is improved by the elite reservation strategy and the congestion adaptive adjustment mechanism. In the case study, comparative experiments with the weighted method and the MOGWO algorithm demonstrate that NSGA-II performs superiorly in key metrics, exhibiting excellent convergence, diversity, and stability. Based on this, a case study is conducted using a chemical industrial park in China as an example, generating 41 sets of weights covering extreme preferences, two-objective balance, and three-objective balance. Decision-makers screen solutions based on loss tolerance thresholds and select the optimal solution using a composite score of comprehensive weighted losses. The study further reveals that improvements in demand satisfaction rates are often accompanied by significant increases in transportation time, while pursuing optimal fairness may weaken overall demand satisfaction levels. Sensitivity analysis confirms that resource demand is the key driver determining the number of feasible solutions, while fairness, as an independent optimization objective, holds irreplaceable importance in emergency scheduling decisions.</p></div>2025-09-30T17:43:26ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332858.g002https://figshare.com/articles/figure/Pareto_frontier_solution_solving_process_/30249403CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302494032025-09-30T17:43:26Z |
| spellingShingle | Pareto frontier solution solving process. Yuhang Wang (332111) Ecology Sociology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified warehouse collaborative supply sensitivity analysis confirms pareto front efficiently loss tolerance thresholds integer planning model holds irreplaceable importance generating 41 sets exhibiting excellent convergence elite reservation strategy optimal solution using mogwo algorithm demonstrate key driver determining ii performs superiorly dynamic cooperative optimization comprehensive weighted losses cause chain accidents independent optimization objective chemical industrial park emergency resource scheduling integrates time efficiency demand satisfaction rates resource allocation fairness ii algorithm chemical accidents resource demand weighted method transportation time search efficiency key metrics emergency dispatching conducted using allocation fairness objective mixed objective balance chemical parks dynamic multi xlink "> standard deviation significant increases puts forward paper constructs often accompanied innovatively quantified high concentration hazardous sources hazard scenarios feasible solutions existing studies demand coverage composite score |
| status_str | publishedVersion |
| title | Pareto frontier solution solving process. |
| title_full | Pareto frontier solution solving process. |
| title_fullStr | Pareto frontier solution solving process. |
| title_full_unstemmed | Pareto frontier solution solving process. |
| title_short | Pareto frontier solution solving process. |
| title_sort | Pareto frontier solution solving process. |
| topic | Ecology Sociology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified warehouse collaborative supply sensitivity analysis confirms pareto front efficiently loss tolerance thresholds integer planning model holds irreplaceable importance generating 41 sets exhibiting excellent convergence elite reservation strategy optimal solution using mogwo algorithm demonstrate key driver determining ii performs superiorly dynamic cooperative optimization comprehensive weighted losses cause chain accidents independent optimization objective chemical industrial park emergency resource scheduling integrates time efficiency demand satisfaction rates resource allocation fairness ii algorithm chemical accidents resource demand weighted method transportation time search efficiency key metrics emergency dispatching conducted using allocation fairness objective mixed objective balance chemical parks dynamic multi xlink "> standard deviation significant increases puts forward paper constructs often accompanied innovatively quantified high concentration hazardous sources hazard scenarios feasible solutions existing studies demand coverage composite score |