A new ranking technique for q‐rung orthopair fuzzy values

Abstract

In intuitionistic fuzzy set and their generalizations such as Pythagorean fuzzy sets and q-rung orthopair fuzzy sets, ranking is not easy to define. There are several techniques available in literature for ranking values in above mentioned orthopair fuzzy sets. It is interesting to see that almost all the proposed ranking methods produce distinct ranking. Notion of knowledge base is very important to study ranking proposed by different techniques. Aim of this paper is to critically analyze the available ranking techniques for q-rung orthopair fuzzy values and propose a new graphical ranking method based on hesitancy index and entropy. Several numerical examples are tested with the proposed technique, which shows that the technique is intuitive and convenient for real life applications. K E Y W O R D S entropy, knowledge base, qROFS, ranking techniques 1 | INTRODUCTION The membership function is employed to represent the information in the fuzzy sets theory. 1 Real-world hesitations can be handled impressively by fuzzy set theory. Atanassov explicated intuitionistic fuzzy set (IFS) as a generalization of the fuzzy set theory. 2 The information in IFS is portrayed in the form of membership (favor) and nonmembership (against) functions. The membership and nonmembership degrees allocate the values from the unit interval [0, 1] with the constraint that their sum is less than or equal to one that is if we represent the associate

q-Rung Orthopair Fuzzy Modified Dissimilarity Measure Based Robust VIKOR Method and its Applications in Mass Vaccination Campaigns in the Context of COVID-19

“…The BIqROFV and WIqROFV are calculated by using (22) and (23). Since c 1 ∈ E b , therefore, ρ + 1 = y 1 = (0.7, 0.8) and ρ − 1 = y 3 = (0.55, 0.9).…”

“…Step 1 is already followed and a qROF decision matrix is obtained in Table 4. In step 2, the BIqROFV ρ + j and WIqROFV ρ − j are computed for each c j ∈ E, by using (22) and (23). The ρ + j and ρ − j are given in Table 5.…”

“…Khan et al [22] defined the knowledge measures for qROFSs. The graphical ranking technique based on hesitancy index and entropy measure was expounded by Khan et al [23]. The MCDM method based on normalized bidirectional projection model and generalized knowledge-based entropy measure in q-rung orthopair fuzzy environment was discussed by Liu et al [24].…”

An axiomatically supported divergence measures for q‐rung orthopair fuzzy sets

“…qROFS has been implemented in numerous areas due to its ability to handle uncertain information more effectively. [10][11][12][13][14][15][16][17][18] It provides the more space to choose orthopair for representing the information than IFSs and PyFSs.…”

Improved cosine and cotangent function-based similarity measures for q-rung orthopair fuzzy sets and TOPSIS method

“…Moreover, a qROFS contains two grades are called truth and falsity with a rule that is the sum of the q th power of both grades is restricted to the unit interval. qROFS has been implemented in numerous areas due to its ability to handle uncertain information more effectively [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]24]. It provides the more space to choose orthopair for representing the information than IFSs and PyFSs.…”