Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

• Published in: Computational & applied mathematics Vol. 42; no. 1
• Main Authors: Zhang, Huiyuan, Wei, Guiwu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2023; Springer Nature B.V
• Subjects: Applications of Mathematics; Applied physics; Computational mathematics; Computational Mathematics and Numerical Analysis; Decision making; Decision theory; Electric vehicle charging; Electric vehicles; Fuzzy sets; Mathematical Applications in Computer Science; Mathematical Applications in the Physical Sciences; Mathematics; Mathematics and Statistics; Sensitivity analysis; Site selection; D-CRITIC method; 91B06; Multiple attribute group decision making; Cumulative prospect theory; Location of electric vehicle charging stations; Spherical fuzzy sets; 90B50; CoCoSo method
• DOI: 10.1007/s40314-022-02183-9
• ISSN: 2238-3603

Location selection of electric vehicle charging stations (LSEVCS) is a complex multi-attribute group decision-making (MAGDM) problem involving multiple experts and multiple conflicting attributes. Spherical fuzzy sets (SFSs) can deeply excavate fuzziness and uncertainty in MAGDM. In this paper, we first propose some new spherical fuzzy distance measures based on Dice and Jaccard indexes to detect the differences between SFSs or inputs. Secondly, considering risk preferences of decision makers, we integrate cumulative prospect theory (CPT) and combined compromise solutions (CoCoSo) method to develop a spherical fuzzy CoCoSo based on CPT (SF-CPT–CoCoSo) model for settling MAGDM issues. At the same time, we extend the improved CRiteria Importance Through Intercriteria Correlation (CRITIC) method, called the distance correlation-based CRITIC (D-CRITIC) method, to reasonably obtain unknown attribute weights under SFSs. Finally, this paper applies the developed model for LSEVCS to verify its practicability. Moreover, sensitivity analysis and comparative discussion with existing methods further demonstrate the robustness and effectiveness of the SF-CPT–CoCoSo model.