Abstract
A (p, q)-fuzzy set is the resulting structure when the fuzzy membership and non-membership values are bounded by a general nonlinear relation. This set enhances the range of depicting uncertain information by making the feasible region larger, and thereby widening the purview of decision-making. Data aggregation is crucial in optimal decision-making. This article attempts to formulate aggregation operators for (p, q)-fuzzy sets using additive generators of strict t-norms and strict t-conorms. The utility of these operators is showcased by examining a decision-making problem, where the best decision is obtained by ranking the alternatives based on their score values. A comparative study is also carried out using some existing aggregation operators to test the validity and effectiveness of the introduced operators.
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Abbreviations
- AO:
-
Aggregation operator
- FFS:
-
Fermatean fuzzy set
- IFS:
-
Intuitionistic fuzzy set
- MADM:
-
Multi-attribute decision-making
- n,m-ROFS:
-
n,m-Rung orthopair fuzzy set
- n,m-ROFWPA:
-
n,m-Rung orthopair fuzzy weighted power average
- PFS:
-
Pythagorean fuzzy set
- (p, q)-FS:
-
(p, q)-Fuzzy set
- (p, q)-FWA:
-
(p, q)-Fuzzy weighted averaging
- (p, q)-FWG:
-
(p, q)-Fuzzy weighted geometric
- (p, q)-FEWA:
-
(p, q)-Fuzzy Einstein weighted averaging
- (p, q)-FEWG:
-
(p, q)-Fuzzy Einstein weighted geometric
- (p, q)-FHWA:
-
(p, q)-Fuzzy Hamacher weighted averaging
- (p, q)-FHWG:
-
(p, q)-Fuzzy Hamacher weighted geometric
- q-ROFS:
-
q-Rung orthopair fuzzy set
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Acknowledgements
The first author gratefully acknowledges the financial assistance from the Ministry of Education of the Government of India and the National Institute of Technology Calicut during the preparation of this paper.
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Sivadas, A., John, S.J. & Athira, T.M. (p, q)-fuzzy aggregation operators and their applications to decision-making. J Anal (2024). https://doi.org/10.1007/s41478-023-00693-1
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DOI: https://doi.org/10.1007/s41478-023-00693-1