Pythagorean fuzzy linguistic Muirhead mean operators and their applications to multi-attribute decision-making

Yi Liu, J. Liu, Ya Qin

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Pythagorean fuzzy sets, as an extension of intuitionistic
fuzzy sets to deal with uncertainty, have attracted much
attention since their introduction, in both theory and
application aspects. In this paper, we investigate multiple
attribute decision‐making (MADM) problems with Pythagorean
linguistic information based on some new aggregation
operators. To begin with, we present some new
Pythagorean fuzzy linguistic Muirhead mean (PFLMM)
operators to deal with MADM problems with Pythagorean
fuzzy linguistic information, including the PFLMM operator,
the Pythagorean fuzzy linguistic‐weighted Muirhead
mean operator, the Pythagorean fuzzy linguistic dual
Muirhead mean operator and the Pythagorean fuzzy
linguistic dual‐weighted Muirhead mean operator. The
main advantages of these aggregation operators are that
they can capture the interrelationships of multiple attributes
among any number of attributes by a parameter vector P
and make the information aggregation process more flexible
by the parameter vector P. In addition, some of the
properties of these new aggregation operators are proved
and some special cases are discussed where the parameter
vector takes some different values. Moreover, we present
two new methods to solve MADM problems with Pythagorean fuzzy linguistic information. Finally, an
illustrative example is provided to show the feasibility and
validity of the new methods, to investigate the influences of
parameter vector P on decision‐making results, and also to
analyze the advantages of the proposed methods by
comparing them with the other existing methods.
Original languageEnglish
Pages (from-to)300
Number of pages332
JournalInternational Journal of Intelligent Systems
Volume35
Issue number2
DOIs
Publication statusPublished - 4 Dec 2019

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