Pythagorean fuzzy multi-criteria decision-making approach based on Spearman rank correlation coefficient

Due to the complexity of objective world, as well as the ambiguity of human thinking, the practical decision-making issues become more and more difficult. Pythagorean fuzzy set is an effective tool for depicting uncertainty of the multi-criteria decision-making problems. This study aims to develop a...

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Vydané v:Soft computing (Berlin, Germany) Ročník 26; číslo 6; s. 3001 - 3012
Hlavní autori: Li, Huimin, Cao, Yongchao, Su, Limin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Predmet:
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Soft Computing in Decision Making and in Modeling in Economics
Pythagorean fuzzy set
Spearman rank correlation coefficient
Decision-making approach
Multi-criteria decision-making
ISSN:1432-7643, 1433-7479
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Shrnutí:Due to the complexity of objective world, as well as the ambiguity of human thinking, the practical decision-making issues become more and more difficult. Pythagorean fuzzy set is an effective tool for depicting uncertainty of the multi-criteria decision-making problems. This study aims to develop a Pythagorean fuzzy multi-criteria decision-making approach to deal with decision-making problem under uncertainty circumstance. Firstly, the concept, representation and related properties of Spearman rank correlation coefficient (SRCC) originated from statistical theory between two PFSs are introduced, which is used to measure the closeness degree between ideal alternative and each alternative. Then, a multi-criteria decision-making approach with Pythagorean fuzzy environment is developed based on the proposed SRCC. Finally, to illustrate the applicability and effectiveness of the proposed method, a real-world infrastructure project decision-making was demonstrated. The result shows that the main advantage of the proposed decision rule would reduce the complexity of the decision-making problem both in theory and practice.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-06615-2