Open AccessProduct evaluation through multi-criteria decision making based on fuzzy parameterized Pythagorean fuzzy hypersoft expert set
Author(s) -
Muhammad Nur Ihsan,
Muhammad Saeed,
Alhanouf Alburaikan,
Hamiden Abd ElWahed Khalifa
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
ISSN - 2473-6988
DOI - 10.3934/math.2022616
Subject(s) - fuzzy logic , fuzzy set operations , parameterized complexity , computer science , fuzzy classification , fuzzy set , set (abstract data type) , fuzzy number , type 2 fuzzy sets and systems , defuzzification , disjoint sets , artificial intelligence , pythagorean theorem , mathematics , data mining , mathematical optimization , algorithm , discrete mathematics , geometry , programming language
In many real-world decision-making situations, uncertain nature of parameters is to be discussed to have unbiased and reliable decisions. Most of the existing literature on fuzzy soft set and its related structures ignored the uncertain parametric attitudes. The concept of fuzzy parameterization is launched to tackle the limitations of existing soft set-like models. Several extensions have already been introduced by using the concept of fuzzy parameterization. In this research, a novel extension, fuzzy parameterized Pythagorean fuzzy hypersoft expert set is aimed to be characterized. This model is more flexible and reliable as compared to existing models because it addresses their insufficiencies for the consideration of multi-argument approximate function. With the entitlement of this function, it tackles the real-life scenarios where each attribute is meant to be further classified into its respective sub-attribute valued disjoint set. The characterization of fuzzy parameterized Pythagorean fuzzy hypersoft expert set is accomplished by employing theoretic, axiomatic and algorithmic approaches. In order to validate the proposed model, an algorithm is proposed to study its role in decision-making while dealing with real-world problem. Moreover, the proposed model is compared with the most relevant existing models to assess its advantageous aspects.