Aczel–Alsina operations‐based linguistic q-rung orthopair fuzzy aggregation operators and their application to site selection of electric vehicle charging station.
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| Title: | Aczel–Alsina operations‐based linguistic q-rung orthopair fuzzy aggregation operators and their application to site selection of electric vehicle charging station. |
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| Authors: | Gurmani, Shahid Hussain1 (AUTHOR) shahidgurmani07@gmail.com, Khan, Muhammad Jabir1 (AUTHOR) jabirkhan.uos@gmail.com, Ding, Weiping1,2 (AUTHOR) dwp9988@163.com, Zulqarnain, Rana Muhammad3 (AUTHOR) ranazulqarnain7778@gmail.com |
| Source: | Engineering Applications of Artificial Intelligence. Aug2025, Vol. 154, pN.PAG-N.PAG. 1p. |
| Subjects: | Electric vehicle charging stations, Group decision making, Fuzzy sets, Electric vehicle industry, Fossil fuels, Electric charge, Aggregation operators |
| Abstract: | Air pollution and climate change caused by fossil fuels adversely affect human health. The transport sector's reliance on fossil fuels intensifies air pollution and climate change. Electric vehicles (EVs) offer a cleaner alternative, reducing emissions and environmental impact significantly. The availability of convenient charging stations is the first priority in supporting the widespread adoption of EVs. The process of site selection for electric vehicle charging stations (EVCSs) plays a crucial role in advancing the EV industry, and this evaluation task typically falls under the domain of Multi-Attribute Group Decision-Making (MAGDM). In this paper, an applicable MAGDM model is designed to evaluate the EVCSs locations. To achieve this, we utilize the concept of linguistic q-rung orthopair fuzzy sets (Lq-ROFS) to represent decision-maker (DM) assessments, which offers improved precision in modeling uncertainty compared to traditional fuzzy sets. Subsequently, we introduce novel Aczel–Alsina operations for Lq-ROFS, including addition, multiplication, scalar multiplication and power operation. This paper discusses that these operations not only satisfy the closure but also satisfy the exchange law and distribution law. Based on these defined operations, we develop various Lq-ROF aggregation operators, including Lq-ROF Aczel–Alsina weighted averaging (Lq-ROFAAWA) operator, Lq-ROF Aczel–Alsina ordered weighted averaging (Lq-ROFAAOWA) operator, Lq-ROF Aczel–Alsina weighted geometric (Lq-ROF AAWG) operator and Lq-ROF Aczel–Alsina ordered weighted geometric (Lq-ROFAAOWG) operator. Each operator is designed with distinct characteristics to address diverse decision-making scenarios effectively. Moreover, we propose a new model dependent on these operators to solve the MAGDM problem. Finally, the applicability of the established approach is demonstrated through a case study related to site selection for EVCSs. The sensitivity and comparative analyses of the model indicate that it has strong effectiveness, performs better than other existing methods, and is largely applicable to a broad range of theoretical and practical problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 185391903 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Aczel–Alsina operations‐based linguistic q-rung orthopair fuzzy aggregation operators and their application to site selection of electric vehicle charging station. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gurmani%2C+Shahid+Hussain%22">Gurmani, Shahid Hussain</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shahidgurmani07@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Muhammad+Jabir%22">Khan, Muhammad Jabir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jabirkhan.uos@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ding%2C+Weiping%22">Ding, Weiping</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dwp9988@163.com</i><br /><searchLink fieldCode="AR" term="%22Zulqarnain%2C+Rana+Muhammad%22">Zulqarnain, Rana Muhammad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> ranazulqarnain7778@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Aug2025, Vol. 154, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br /><searchLink fieldCode="DE" term="%22Group+decision+making%22">Group decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+vehicle+industry%22">Electric vehicle industry</searchLink><br /><searchLink fieldCode="DE" term="%22Fossil+fuels%22">Fossil fuels</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+charge%22">Electric charge</searchLink><br /><searchLink fieldCode="DE" term="%22Aggregation+operators%22">Aggregation operators</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Air pollution and climate change caused by fossil fuels adversely affect human health. The transport sector's reliance on fossil fuels intensifies air pollution and climate change. Electric vehicles (EVs) offer a cleaner alternative, reducing emissions and environmental impact significantly. The availability of convenient charging stations is the first priority in supporting the widespread adoption of EVs. The process of site selection for electric vehicle charging stations (EVCSs) plays a crucial role in advancing the EV industry, and this evaluation task typically falls under the domain of Multi-Attribute Group Decision-Making (MAGDM). In this paper, an applicable MAGDM model is designed to evaluate the EVCSs locations. To achieve this, we utilize the concept of linguistic q-rung orthopair fuzzy sets (Lq-ROFS) to represent decision-maker (DM) assessments, which offers improved precision in modeling uncertainty compared to traditional fuzzy sets. Subsequently, we introduce novel Aczel–Alsina operations for Lq-ROFS, including addition, multiplication, scalar multiplication and power operation. This paper discusses that these operations not only satisfy the closure but also satisfy the exchange law and distribution law. Based on these defined operations, we develop various Lq-ROF aggregation operators, including Lq-ROF Aczel–Alsina weighted averaging (Lq-ROFAAWA) operator, Lq-ROF Aczel–Alsina ordered weighted averaging (Lq-ROFAAOWA) operator, Lq-ROF Aczel–Alsina weighted geometric (Lq-ROF AAWG) operator and Lq-ROF Aczel–Alsina ordered weighted geometric (Lq-ROFAAOWG) operator. Each operator is designed with distinct characteristics to address diverse decision-making scenarios effectively. Moreover, we propose a new model dependent on these operators to solve the MAGDM problem. Finally, the applicability of the established approach is demonstrated through a case study related to site selection for EVCSs. The sensitivity and comparative analyses of the model indicate that it has strong effectiveness, performs better than other existing methods, and is largely applicable to a broad range of theoretical and practical problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2025.110989 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Electric vehicle charging stations Type: general – SubjectFull: Group decision making Type: general – SubjectFull: Fuzzy sets Type: general – SubjectFull: Electric vehicle industry Type: general – SubjectFull: Fossil fuels Type: general – SubjectFull: Electric charge Type: general – SubjectFull: Aggregation operators Type: general Titles: – TitleFull: Aczel–Alsina operations‐based linguistic q-rung orthopair fuzzy aggregation operators and their application to site selection of electric vehicle charging station. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gurmani, Shahid Hussain – PersonEntity: Name: NameFull: Khan, Muhammad Jabir – PersonEntity: Name: NameFull: Ding, Weiping – PersonEntity: Name: NameFull: Zulqarnain, Rana Muhammad IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 154 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
| ResultId | 1 |
