Multiple attribute group decision making based on quasirung orthopair fuzzy sets: Application to electric vehicle charging station site selection problem

Abstract

This paper aims to greatly improve decision makers’ capability of capturing their judgment in a broader space. In order to achieve this, we introduce the notion of a p,q-quasirung orthopair fuzzy set (p,q-QOFS), which is an extension of the q-rung orthopair fuzzy set. In p,q-QOFS, the sum of the pth power of membership degree and qth power of nonmembership degree is less than or equal to 1, where p and q are natural numbers. Thus, due to the additional parameter p, the p,q-QOFS can express incomplete information more flexibly and elaborately. This paper first develops the concept of p,q-QOFS and establishes that it is an extension of several existing fuzzy sets. Then, we introduce the score and accuracy functions of p,q-QOFS and analyze a few mathematical properties. Next, we define the Hamming distance measure between two p,q-QOFSs and some important properties. After that, we investigate the basic operations of p,q-QOFSs and extend these operational laws to aggregation operators. Further, we introduce the weighted averaging and geometric aggregation operators to aggregate p,q-quasirung orthopair fuzzy data. Moreover, we establish a p,q-quasirung orthopair fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for solving multi-attribute group decision-making problems with unknown attribute weights. The present study also discusses a case study illustrating the applicability of the method through the selection of the most appropriate site for an electric vehicle charging station in an Indian city. In this case study, we consider seven alternative sites, including Raniganj, Jamuria, Kulti, and Burnpur. As a result of this study, Jamuria appears to be the best location to build an electric vehicle charging station. Finally, we illustrate the validity and practicability of our developed method through a comparative analysis with existing methods.