Hesitant fuzzy power Maclaurin symmetric mean operators in the framework of Dempster-Shafer theory for multiple criteria decision making

Yanru Zhong, Liangbin Cao, Huanan Zhang, Yuchu Qin, Meifa Huang, Xiaonan Luo

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a hesitant fuzzy power Maclaurin symmetric mean operator and a hesitant fuzzy weighted power Maclaurin symmetric mean operator are presented. The properties of these operators are explored and proved, and their special cases are discussed. Based on the presented operators, a new method is proposed to solve the multiple criteria decision making problems with hesitant fuzzy numbers. A numerical example is introduced to elucidate the application of the method. The advantages of the method are demonstrated via comparisons with some of the existing methods under a set of numerical examples. The demonstration results show that the proposed method has the capabilities to reduce the influence of biased evaluation values and consider the interrelationships between criteria, and more importantly it is free of the limitations of conventional operational laws of hesitant fuzzy numbers and appliable for the multiple criteria decision making problems where criterion weights are in the form of hesitant fuzzy numbers
Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Publication statusAccepted/In press - 27 Jan 2021

Fingerprint Dive into the research topics of 'Hesitant fuzzy power Maclaurin symmetric mean operators in the framework of Dempster-Shafer theory for multiple criteria decision making'. Together they form a unique fingerprint.

Cite this