Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10701
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dc.contributor.authorAslan Özşahin, Selcen Gülsüm-
dc.contributor.authorErdebilli, Babek-
dc.date.accessioned2023-10-24T07:01:44Z-
dc.date.available2023-10-24T07:01:44Z-
dc.date.issued2023-
dc.identifier.issn2192-4376-
dc.identifier.issn2192-4384-
dc.identifier.urihttps://doi.org/10.1016/j.ejtl.2023.100118-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10701-
dc.description.abstractEurope strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only if electric vehicles dominate the transportation sector. Paving the way for electric vehicle deployment on roads is subject to the provision of electric-vehicle-charging stations on the roads such that sufficiently good driving experience without any obstacles can be achieved. To address this timely societal challenge, we proposed a novel methodology by using the well-known facility-location-allocation methodology named set-covering location models with statistical machine learning and developed it for the problem settings of identifying electric-vehicle-charging station locations. Statistical machine learning was employed in the proposed model to more precisely identify and determine feasible coverage sets. We demonstrated the efficiency of the proposed model for the Capital Region of Denmark, where the green transition is part of the political agenda and is of severe societal concern, by using the newly collected main road transportation dataset.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEuro Journal On Transportation And Logisticsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGreen transportationen_US
dc.subjectGreen transitionen_US
dc.subjectIntelligent optimizationen_US
dc.subjectML in SCLMen_US
dc.subjectML-based covering problemsen_US
dc.subjectStatistical-machine-learning-based intelligenten_US
dc.subjectoptimizationen_US
dc.subjectData-driven optimizationen_US
dc.subjectIntelligent relaxationen_US
dc.subjectInfrastructureen_US
dc.subjectOptimizationen_US
dc.subjectDeploymenten_US
dc.titleStatistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehiclesen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume12en_US
dc.identifier.wosWOS:001075253500001en_US
dc.identifier.scopus2-s2.0-85169831086en_US
dc.institutionauthor-
dc.identifier.doi10.1016/j.ejtl.2023.100118-
dc.authorscopusid58562839300-
dc.authorscopusid57221604869-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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