The differential measure for Pythagorean fuzzy multiple criteria group decision-making

Abstract

Pythagorean fuzzy sets (PFSs) proved to be powerful for handling uncertainty and vagueness in multi-criteria group decision-making (MCGDM). To make a compromise decision, comparing PFSs is essential. Several approaches were introduced for comparison, e.g., distance measures and similarity measures. Nevertheless, extant measures have several defects that can produce counter-intuitive results, since they treat any increase or decrease in the membership degree the same as the non-membership degree; although each parameter has a different implication. This study introduces the differential measure (DFM) as a new approach for comparing PFSs. The main purpose of the DFM is to eliminate the unfair arguments resulting from the equal treatment of the contradicting parameters of a PFS. It is a preference relation between two PFSs by virtue of position in the attribute space and according to the closeness of their membership and non-membership degrees. Two PFSs are classified as identical, equivalent, superior, or inferior to one another giving the degree of superiority or inferiority. The basic properties of the proposed DFM are given. A novel method for multiple criteria group decision-making is proposed based on the introduced DFM. A new technique for computing the weights of the experts is developed. The proposed method is applied to solve two applications, the evaluation of solid-state drives and the selection of the best photovoltaic cell. The results are compared with the results of some extant methods to illustrate the applicability and validity of the method. A sensitivity analysis is conducted to examine its stability and practicality.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

“…In research of sharaf, he found the difference between support and opposition. This research [39] encourages us to find differences among three variables.…”

“…The outcomes of other decision-making methodologies, including the TOPSIS method proposed by Zhang et al [47], the TODIM approach developed by Ren et al [48], the distance and similarity measure introduced by Zeng et al [49], the fuzzy weighted and ordered weighted aggregation operators presented by Garg [50], and the differential measure for pythagorean fuzzy sets, are compared with the outcomes of the proposed methodology. Table 4 displays the rankings that these techniques produced [39]. Table 4 demonstrates that the suggested technique produced the same optimal alternative as the ones previously employed.…”

“…Additionally, a better VIKOR method is presented to address the uncertainty in categorising viral hepatitis. This article introduces the idea of differential measure (DFM), derived from [39] as a novel method for contrasting spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) [40]. When the uncertainty grade has equally in both sets, there is a preference connection between two Sq-LDFS because of their relative positions in the attribute space and the proximity of their membership and nonmembership degrees.…”