SMAA‐QUALIFLEX methodology to handle multicriteria decision‐making problems based on q‐rung fuzzy set with hierarchical structure of criteria using bipolar Choquet integral

Banerjee, Debasmita; Guha, Debatosh; Martínez, Luis

doi:10.1002/int.22210

Cited by 34 publications

(16 citation statements)

References 52 publications

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“…The generalized Maclaurin symmetric mean operators, the exponential aggregation operators, the aggregation operators, the interaction Hamy mean operators, the Dombi power partitioned Heronian mean operators, the power Maclaurin symmetric mean operators, the Heronian mean operators, the neutrality aggregation operators, the prioritized aggregation operators, the robust Einstein prioritized aggregation operators, the robust information aggregation operations, the power aggregation operators for

sans-serifqROFSs

were expounded in References [9–20], respectively. The multiple heterogeneous relationships between membership functions and criterion were explored by Yang et al 21 The

sans-serifqROF

SMAA‐QUALIFLEX method was proposed by Banerjee et al 22 and applied to multiattribute decision making (MADM) problems. Peng et al 23 proposed an algorithm for emergency decision making using

sans-serifqROFSs

.…”

Section: Introductionmentioning

confidence: 99%

Knowledge measure for the q‐rung orthopair fuzzy sets

2020

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sans-serifqROFSs

were expounded in References [9–20], respectively. The multiple heterogeneous relationships between membership functions and criterion were explored by Yang et al 21 The

sans-serifqROF

SMAA‐QUALIFLEX method was proposed by Banerjee et al 22 and applied to multiattribute decision making (MADM) problems. Peng et al 23 proposed an algorithm for emergency decision making using

sans-serifqROFSs

.…”

Section: Introductionmentioning

confidence: 99%

Knowledge measure for the q‐rung orthopair fuzzy sets

2020

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“…Liu et al 14 handled multiattribute decision making (MADM) problems by generalized Maclaurin symmetric mean operators based on qROFSs. The qROFS SMAA‐QUALIFLEX method was proposed by Banerjee et al 15 Peng et al 16 proposed an algorithm for emergency decision making using qROFSs. Xing et al solved MADM problems by using interaction Hamy mean operators based on qROFSs 17 .…”

Section: Introductionmentioning

confidence: 99%

A new ranking technique for q‐rung orthopair fuzzy values

Khan

Ali

2020

Int J Intell Syst

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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

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“…Wang et al [25] presented q-rung orthopair fuzzy Hamy mean operators. Some q-rung orthopair fuzzy distance measures, entropy measures, correlation measures are studied [28]- [36]. Rajkumar [28] defined the q-rung orthopair fuzzy divergence and entropy measures.…”

Section: Introductionmentioning

confidence: 99%

“…Some multiple attribute decision making methods have been presented [37]- [40]. Banerjee et al [36] developed a q-rung orthopair fuzzy method based on the multiple criteria hierarchy process method and QUAL-IFLEX method. Liu and Huang [37] defined a new correlation measure based on linguistic scale function and developed a consensus reaching process for probabilistic linguistic q-rung orthopair fuzzy TOPSIS method.…”

Section: Introductionmentioning

confidence: 99%