Einstein Aggregation Operators with Cubic Fermatean Fuzzy Sets

The study goals are to address the compound, multi-criteria decision-making tests associated with choosing the ideal scope for electric vehicle charging stations. This difficulty ascends due to the interaction of numerous quantitative and qualitative issues and the uncertainty characteristic in such assessments. Aliya et al. [37] introduced the concept of the cubic fermatean fuzzy sets, including their operational laws and score function. We develop the three aggregation operators including CFEFWA, CFEFOWA, and CFEFHWA operators with the cubic fermatean fuzzy set. Furthermore, uncertainty usually arises in Cubic Fermatean fuzzy sets and challenges related to selecting charging stations for electric vehicles. Therefore, the current study aims to provide an integrated decision-making method for handling multi-criteria CFFS electric vehicle charging stations. For CFFS, we propose some innovative Einstein operations to aggregate the decision data. To illustrate the effectiveness of our proposed technique, we apply it to an applied case of choosing EV charging positions and associate the outcomes with existing practices. The efficiency of the proposed method is assessed by comparing its fallouts with those obtained from existing methodologies. The results indicate that the introduced technique proposes extra value and is well aligned with current observations. Introduce three aggregation operators based on cubic fermatean fuzzy set to improve the decision-making for EV charging station locations. Propose an MCDM method with cubic fermatean fuzzy set To define the effectiveness of the proposed method through a practical case study and numerical example