Abstract
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.