Power Muirhead mean operators of interval-valued intuitionistic fuzzy values in the framework of Dempster-Shafer theory for multiple criteria decision-making

Yanru Zhong, Huanan Zhang, Liangbin Cao, Yiyuan Li, Yuchu Qin, Xiaonan Luo

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Multiple criteria decision-making (MCDM) based on interval intuitionistic fuzzy value (IVIFV) is a process of aggregating decision criteria represented by multiple interval-valued intuitionistic fuzzy numbers to select the optimal alternative. Among them, an aggregation operator is an indispensable tool, and the properties of an aggregation operator directly affect the decision results. Existing aggregation operators based on IVIFV have satisfactory results in eliminating the correlation between criteria and removing the influence of outliers on the results. However, there are some unreasonable results due to some undesired properties of IVIFVs. In this paper, IVIFV operation under the Dempster-Shafer theory (DST) framework is applied to combine the power average and Muirhead mean operators and interval intuitionistic fuzzy power Muirhead mean operators under DST framework are presented. Then a method based on the presented operators for MCDM problems is proposed. Finally, a set of numerical experiments are conducted to demonstrate the proposed method. The experimental results suggest that the proposed method not only retains the robustness of the power average operator and the capability of the Muirhead mean operator, but also eliminates a shortcoming that existing interval intuitionistic fuzzy operators cannot handle the case where the weights are in IVIFVs.
Original languageEnglish
Pages (from-to)763-782
Number of pages20
JournalSoft Computing
Volume27
Issue number2
Early online date14 Nov 2022
DOIs
Publication statusPublished - 1 Jan 2023

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