A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection

Abstract

Purpose:Since the pollution from the transportation sector has become intolerable in urban areas, many authorities are beginning to ban vehicles powered by fossil fuels. In this scenario, electric vehicle (EV) mobility is the only viable option for the transportation sector. Despite the fact that EVs offer a number of advantages over fuel-powered vehicles, it can be challenging for a purchaser to select a model due to its technical features and their lack of knowledge about EVs. This article aims to assist purchasers of EV by constructing an MCDM problem with multiple features as criteria and EV models as alternatives. Design/methodology/approach:The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks alternatives to a multi-criteria decision-making (MCDM) problem based on the distance between the positive and negative ideal solutions. Even though the TOPSIS methodology has several fuzzy enhancements, it is difficult to select an appropriate distance measure in an uncertain context. To replace the distance measure, we introduced an improved correlation coefficient measure in fermatean fuzzy contexts by considering the hesitancy function of fermatean fuzzy sets (FFS). First, we establish the proposed correlation coefficient by defining its main characteristics, including the weighted correlation coefficient, type I and type II closeness measures, and the weighted index coefficient. Then, the TOPSIS method is extended using the proposed correlation measure in a fermatean fuzzy environment. There is an algorithm that presents the proposed fermatean fuzzy TOPSIS (FF-TOPSIS) method. The proposed approach is used to determine the ranking order of the EVs. To assist the decision-maker, the sensitivity of the proposed technique is investigated by varying its parameters. Finally, the consistency of the proposed FF-TOPSIS is evaluated by comparing it to existing crisp as well as fuzzy MCDM methods. Findings:By implementing the FF-TOPSIS approach, it has been determined that the fifth EV model is the most favorable option, while the first model is deemed the least desirable choice. The primary factors to evaluate an EV are its range and its price, rather than battery life, storage, and charging time. Originality:This article presents a novel correlation coefficient for FFSs, which incorporates the hesitancy function into the calculation for the first time. The correlation-based FF-TOPSIS method is a novel extension of the TOPSIS technique in fermatean fuzzy systems.