Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114587
DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorLi, Hen_US
dc.creatorWu, Yen_US
dc.creatorXin, Ben_US
dc.creatorXu, Men_US
dc.creatorSu, Sen_US
dc.date.accessioned2025-08-12T09:09:55Z-
dc.date.available2025-08-12T09:09:55Z-
dc.identifier.citationv. 198, 104107-
dc.identifier.issn1366-5545en_US
dc.identifier.otherv. 198, 104107-
dc.identifier.urihttp://hdl.handle.net/10397/114587-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.subjectCarbon neutralityen_US
dc.subjectHealthcare operations managementen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectPatient flowen_US
dc.subjectΕ-constraint methoden_US
dc.titleOptimal operational and carbon neutrality strategies for private hospitals : a multi-objective approach considering patient flowen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume198en_US
dc.identifier.doi10.1016/j.tre.2025.104107en_US
dcterms.abstractHospitals are now the fifth-largest source of global greenhouse gas (GHG) emissions. Despite this significant impact, the critical goal of achieving carbon neutrality is often overlooked in optimization studies on healthcare operations management. To address this gap, this paper proposes a novel multi-objective decision-making framework for private hospitals that balances patient health, environmental impact, and economic performance. This study tackles the intricate challenge of multi-objective optimization that maximizes patient utility and hospital profit while simultaneously minimizing net carbon emissions. An exact algorithm based on the ε-constraint method is employed to optimize hospital operational decisions by discretizing patient utility and net carbon emission targets into linear constraints on the non-concave profit objective. This approach guarantees Pareto-optimal solutions, thus enabling hospital decision-makers to select their preferred options among the three criteria: patient utility, environmental sustainability, and hospital profitability, while exploring the resulting trade-offs. Additionally, this study discusses the impacts of hospital operational decision changes on these objectives, particularly under scenarios where patient flow increases waiting time due to limited hospital capacity. To validate the practical significance of the proposed multi-objective optimization model and evaluate the effectiveness of the proposed algorithm, two case studies and sensitivity analyses are conducted. The results demonstrate that this approach effectively supports hospital decision-makers in making informed decisions that balance patient utility, carbon neutrality, and hospital profitability objectives.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportation research. Part E, Logistics and transportation review, June 2025, v. 198, 104107en_US
dcterms.isPartOfTransportation research. Part E, Logistics and transportation reviewen_US
dcterms.issued2025-06-
dc.identifier.eissn1878-5794en_US
dc.identifier.artn104107en_US
dc.description.validate202508 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera3980-
dc.identifier.SubFormID51865-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe work was supported in part by the Hong Kong Research Grants Council under Grants 15508021 and 15511424, and the NSFC/RGC Joint Research Scheme under Grant No. 72261160393 & N_PolyU590/22.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2028-06-30en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2028-06-30
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