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Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model

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dc.citedby9
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorDin�er H.en_US
dc.contributor.authorY�ksel S.en_US
dc.contributor.authorMohamad Sharaf I.en_US
dc.contributor.authorid57205435311en_US
dc.contributor.authorid57201013684en_US
dc.contributor.authorid55734383000en_US
dc.contributor.authorid57201009814en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid55567227600en_US
dc.contributor.authorid57190620397en_US
dc.contributor.authorid17435789800en_US
dc.date.accessioned2025-03-03T07:42:10Z
dc.date.available2025-03-03T07:42:10Z
dc.date.issued2024
dc.description.abstractDue to energy's global reliance on fossil fuels and population growth, Greenhouse gas (GHG) emissions and their repercussions have attracted attention. Due to their cheaper cost and cleaner environment, renewable energy modes of transportation like electric vehicles are highly sought after. Electric vehicles are beneficial, but they also emit emissions indirectly in power plants that generate their electricity, which could affect small and medium communities. Thus, it is crucial to assess such modes of transportation's performance while considering key aspects and criteria. However, scholarly works in this field have not fully addressed the deployment of a comprehensive electric vehicle decision-making support system. This study addresses electric bus selection by introducing a novel approach to Multi Criteria Decision Making (MCDM) utilizing a developed integrated fuzzy set. We introduce an integrated approach that combines an Entropy weighting approach with a 2-tuple Linguistic T-Spherical Fuzzy Decision by Opinion Score Method (2TLTS-FDOSM). This approach is designed to tackle the challenges associated with evaluating the feasibility of electric bus models (EBMs) and addressing the theoretical challenge of MCDM in the context of the presented case study. These challenges include dealing with ambiguities and inconsistencies among decision-makers. The former method is utilized to ascertain the significance of assessment criteria, whereas the latter approach is applied to select the most favorable EBM by utilizing the weights obtained. As for the 2TLTS-FDOSM results, out of all the (n = 6) EBMs considered, A3 (11-E) EBM obtained the highest score value, while the A3 (9-E) EBM had the lowest score. The robustness of the results is confirmed through sensitivity analysis. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo123498
dc.identifier.doi10.1016/j.eswa.2024.123498
dc.identifier.scopus2-s2.0-85185558475
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185558475&doi=10.1016%2fj.eswa.2024.123498&partnerID=40&md5=e3ec1eec582ab2610ffb52d0ef5a242c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36383
dc.identifier.volume249
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Hybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleExpert Systems with Applications
dc.subjectDecision making
dc.subjectEntropy
dc.subjectFossil fuels
dc.subjectFuzzy sets
dc.subjectGas emissions
dc.subjectGas plants
dc.subjectGlobal warming
dc.subjectGreenhouse gases
dc.subjectLinguistics
dc.subjectPopulation statistics
dc.subjectSensitivity analysis
dc.subjectUncertainty analysis
dc.subject2-tuple linguistic
dc.subject2-tuple linguistic T-spherical fuzzy set
dc.subjectElectric bus
dc.subjectElectric bus model
dc.subjectFDOSM
dc.subjectFuzzy decision
dc.subjectMulti criteria decision-making
dc.subjectMulticriteria decision-making
dc.subjectMulticriterion decision makings
dc.subjectSmall and medium sized community
dc.subjectElectric buses
dc.titleSelection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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