Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model
Due 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. Ele...
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my.uniten.dspace-363832025-03-03T15:42:10Z Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model Alamoodi A.H. Albahri O.S. Deveci M. Albahri A.S. Yussof S. Din�er H. Y�ksel S. Mohamad Sharaf I. 57205435311 57201013684 55734383000 57201009814 16023225600 55567227600 57190620397 17435789800 Decision making Entropy Fossil fuels Fuzzy sets Gas emissions Gas plants Global warming Greenhouse gases Linguistics Population statistics Sensitivity analysis Uncertainty analysis 2-tuple linguistic 2-tuple linguistic T-spherical fuzzy set Electric bus Electric bus model FDOSM Fuzzy decision Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Small and medium sized community Electric buses Due 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) Final 2025-03-03T07:42:10Z 2025-03-03T07:42:10Z 2024 Article 10.1016/j.eswa.2024.123498 2-s2.0-85185558475 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185558475&doi=10.1016%2fj.eswa.2024.123498&partnerID=40&md5=e3ec1eec582ab2610ffb52d0ef5a242c https://irepository.uniten.edu.my/handle/123456789/36383 249 123498 All Open Access; Hybrid Gold Open Access Elsevier Ltd Scopus |
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Decision making Entropy Fossil fuels Fuzzy sets Gas emissions Gas plants Global warming Greenhouse gases Linguistics Population statistics Sensitivity analysis Uncertainty analysis 2-tuple linguistic 2-tuple linguistic T-spherical fuzzy set Electric bus Electric bus model FDOSM Fuzzy decision Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Small and medium sized community Electric buses |
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Decision making Entropy Fossil fuels Fuzzy sets Gas emissions Gas plants Global warming Greenhouse gases Linguistics Population statistics Sensitivity analysis Uncertainty analysis 2-tuple linguistic 2-tuple linguistic T-spherical fuzzy set Electric bus Electric bus model FDOSM Fuzzy decision Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Small and medium sized community Electric buses Alamoodi A.H. Albahri O.S. Deveci M. Albahri A.S. Yussof S. Din�er H. Y�ksel S. Mohamad Sharaf I. Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
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Due 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) |
| author2 |
57205435311 |
| author_facet |
57205435311 Alamoodi A.H. Albahri O.S. Deveci M. Albahri A.S. Yussof S. Din�er H. Y�ksel S. Mohamad Sharaf I. |
| format |
Article |
| author |
Alamoodi A.H. Albahri O.S. Deveci M. Albahri A.S. Yussof S. Din�er H. Y�ksel S. Mohamad Sharaf I. |
| author_sort |
Alamoodi A.H. |
| title |
Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
| title_short |
Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
| title_full |
Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
| title_fullStr |
Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
| title_full_unstemmed |
Selection of electric bus models using 2-tuple linguistic T-spherical fuzzy-based decision-making model |
| title_sort |
selection of electric bus models using 2-tuple linguistic t-spherical fuzzy-based decision-making model |
| publisher |
Elsevier Ltd |
| publishDate |
2025 |
| _version_ |
1825816061071589376 |
| score |
13.244109 |