Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power Heronian aggregation operators. (June 2017)

• Record Type: Journal Article
• Authors: Liu, Peide
• Is Part Of: Computers & industrial engineering. Volume 108(2017)
• Page Start: 199
• Page End: 212
• Publication Date: 2017-06
• Subjects: Multiple attribute group decision making -- Interval-valued intuitionistic fuzzy set -- Power average operator -- Heronian mean -- Interval-valued intuitionistic fuzzy power weighted Heronian aggregation (IVIFPWHA) operator; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.04.033
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We proposed some PHA operators by combining power-average and Heronian mean. We investigated some desirable properties of these operators. We studied some special cases of these operators. We developed a MAGDM method based these operators. We gave the advantages of proposed method by comparing with the existing methods. Abstract: The power average operator can relieve the some influences of unreasonable data given by biased decision makers, and Heronian mean operator can consider the interrelationship of the aggregated arguments. In order to take full advantages of these two kinds of operators, in this paper, we combined the power average operator with Heronian mean operator and extended them to process interval-valued intuitionistic fuzzy information, and proposed the interval-valued intuitionistic fuzzy power Heronian aggregation (IVIFPHA) operator, interval-valued intuitionistic fuzzy power weight Heronian aggregation (IVIFPWHA) operator. At the same time, we presented a new similarity function of IVIFNs as support degree in power weighting, and it has a good reliability and accuracy. Further, some properties of these new aggregation operators are investigated and some special cases are discussed, and a new technique based on these operators for fuzzy multiple attribute group decision making (MAGDM) was presented. Finally, an illustrative example was given to illustrate the effectiveness and advantages of the developed method by comparing with the existing methods.