Product evaluation through multi-criteria decision making based on fuzzy parameterized Pythagorean fuzzy hypersoft expert set

Muhammad Ihsan; Muhammad Saeed; Alhanouf Alburaikan; Hamiden Abd El-Wahed Khalifa; Muhammad Ihsan; Muhammad Saeed; Alhanouf Alburaikan; Hamiden Abd El-Wahed Khalifa

doi:10.3934/math.2022616

AIMS Mathematics

2022, Volume 7, Issue 6: 11024-11052. doi: 10.3934/math.2022616

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Research article

Product evaluation through multi-criteria decision making based on fuzzy parameterized Pythagorean fuzzy hypersoft expert set

1.
Department of Mathematics, University of Management and Technology, Lahore, Pakistan
2.
Department of Mathematics, College of Science and Arts, Al-Badaya, Qassim University, Buraydah, Saudi Arabia
3.
Department of Operations Research, Faculty of Graduate Studies for Statistical Research, Cairo University Giza, Egypt

Received: 07 February 2022 Revised: 21 March 2022 Accepted: 24 March 2022 Published: 06 April 2022
MSC : 03B52, 03E72, 03E75

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.
- soft set,
- fuzzy soft set,
- Pythagorean fuzzy soft set,
- hypersoft set,
- hypersoft expert set
Citation: Muhammad Ihsan, Muhammad Saeed, Alhanouf Alburaikan, Hamiden Abd El-Wahed Khalifa. Product evaluation through multi-criteria decision making based on fuzzy parameterized Pythagorean fuzzy hypersoft expert set[J]. AIMS Mathematics, 2022, 7(6): 11024-11052. doi: 10.3934/math.2022616

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Abstract

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.

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