Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection

• Published in: Expert systems with applications Vol. 182; p. 115267
• Main Authors: Rani, Pratibha, Mishra, Arunodaya Raj
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.11.2021; Elsevier BV
• Subjects: Agglomeration; Decision making; Deviation; Divergence measure; Einstein aggregation operators; Electric vehicle charging; Electric vehicle charging station (EVCS); Electric vehicles; Fermatean fuzzy set; Fuzzy logic; Fuzzy sets; Maximization; MCDM; MULTIMOORA; Multiple criterion; Multiple objective analysis; Operators; Optimization; Site selection; Fermatean fuzzy set; Divergence measure; MCDM; MULTIMOORA; Electric vehicle charging station (EVCS); Einstein aggregation operators
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115267

•A novel divergence measure is proposed for Fermatean fuzzy sets.•Based on Einstein operations, some Fermatean fuzzy Einstein weighted operators are proposed.•New MULTIMOORA method is proposed for solving MCDM problems with Fermatean fuzzy information.•Case study of a EVCS location selection is presented to show the potentiality of the proposed method. The optimal location of electric vehicle charging station (EVCS) will promote the rapid development of the electric vehicle (EV) industry. Generally, EVCS location selection is treated as complex uncertain multi-criteria decision-making (MCDM) problem because of the existence of many quantitative and qualitative influencing factors. Moreover, uncertainty is usually occurred in EVCS location selection problem and Fermatean fuzzy set (FFS), as an expansion of Pythagorean fuzzy set, can effectively handle the ambiguity by reducing human intervention. Thus, the aim of the current study is to design an integrated decision making method for solving multi-criteria EVCS location selection problem under FFS context. This method is based on multi-objective optimization based on the ratio analysis with the full multiplicative form (MULTIMOORA) approach, maximizing deviation method and Einstein aggregation operators within Fermatean fuzzy environment. At the same time, the criteria weights are determined through the maximizing deviation method. For this, we introduce a divergence measure for FFSs. To aggregate the decision information, we propose some novel Einstein operations for FFS. In light of these operational laws, we further suggest some Fermatean fuzzy Einstein aggregation operators and their enviable characteristics. To illustrate the potentiality and usefulness of the present methodology, we carry out an illustrative study of EVCS location selection problem with FFS setting. Comparing the present MULTIMOORA framework with the extant methods confirms the strength of the obtained outcomes. The findings conclude that the introduced method is more useful and well-consistent with extant methods.