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Original scientific paper

https://doi.org/10.1080/1331677X.2019.1638808

Pythagorean fuzzy Muirhead mean operators in multiple attribute decision making for evaluating of emerging technology commercialization

Xiyue Tang ; School of Business, Sichuan Normal University, Chengdu, P.R. China
Guiwu Wei ; School of Business, Sichuan Normal University, Chengdu, P.R. China
Hui Gao ; School of Business, Sichuan Normal University, Chengdu, P.R. China


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Abstract

In today’s world, with the advancement of technology, several emerging technologies are coming. Faced with massive emerging technologies which are the component of the technology pool, how to identify the commercial potential of emerging technologies in theory and practice is an important problem. The scientific approach to the selection of these emerging technologies is one of the main objectives of the research. In this paper, we extend Muirhead mean (MM) operator and dual MM (DMM) operator to process the Pythagorean fuzzy numbers (PFNs) and then to solve the multiple attribute decision making (MADM) problems. Firstly, we develop some Pythagorean fuzzy Muirhead mean operators by extending MM and DMM operators to Pythagorean fuzzy information. Then, we prove some properties and discuss some special cases with respect to the parameter vector. Moreover, we present some new methods to deal with MADM problems with the PFNs based on the proposed MM and DMM operators. Finally, we verify the validity and reliability of our methods by using an application example for potential evaluation of emerging technology commercialization, and analyze the advantages of our methods by comparing with other existing methods

Keywords

Multiple attribute decision making (MADM); Muirhead mean (MM) operator; dual Muirhead mean (DMM) operator; Pythagorean fuzzy number; Pythagorean Fuzzy Muirhead mean (PFMM) operator; Pythagorean Fuzzy dual Muirhead mean (PFDMM) operator; potential evaluation; emerging technology commercialization

Hrčak ID:

228839

URI

https://hrcak.srce.hr/228839

Publication date:

22.1.2019.

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