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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.
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]
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Database: Engineering Source
Description
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]
ISSN:09521976
DOI:10.1016/j.engappai.2025.110989