4.6 Article

Multiple attribute group decision-making based on cubic linguistic Pythagorean fuzzy sets and power Hamy mean

Journal

COMPLEX & INTELLIGENT SYSTEMS
Volume 7, Issue 3, Pages 1673-1693

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s40747-020-00255-z

Keywords

Cubic linguistic Pythagorean fuzzy set; Power average operator; Hamy mean; Cubic linguistic Pythagorean fuzzy power Hamy mean; Multiple attribute group decision-making

Funding

  1. National Natural Science Foundation of China [61702023]
  2. Humanities and Social Science Foundation of Ministry of Education of China [17YJC870015]
  3. Beijing Natural Science Foundation [7192107]

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This paper extends linguistic Pythagorean fuzzy sets to cubic LPFSs and studies a MAGDM method based on CLPFSs. The new aggregation operators effectively and comprehensively aggregate attribute values in MAGDM problems, and a new MAGDM method based on CLPFSs is proposed.
The linguistic Pythagorean fuzzy sets (LPFSs), which employ linguistic terms to express membership and non-membership degrees, can effectively deal with decision makers' complicated evaluation values in the process of multiple attribute group decision-making (MAGDM). To improve the ability of LPFSs in depicting fuzzy information, this paper generalized LPFSs to cubic LPFSs (CLPFSs) and studied CLPFSs-based MAGDM method. First, the definition, operational rules, comparison method and distance measure of CLPFSs are investigated. The CLPFSs fully adsorb the advantages of LPFSs and cubic fuzzy sets and hence they are suitable and flexible to depict attribute values in fuzzy and complicated decision-making environments. Second, based on the extension of power Hamy mean operator in CLPFSs, the cubic linguistic Pythagorean fuzzy power average operator, the cubic linguistic Pythagorean fuzzy power Hamy mean operator as well as their weighted forms were introduced. These aggregation operators can effectively and comprehensively aggregate attribute values in MAGDM problems. Besides, some important properties of these operators were studied. Finally, we presented a new MAGDM method based on CLPFSs and their aggregation operators. Illustrative examples and comparative analysis are provided to show the effectiveness and advantages of our proposed decision-making method.

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