Multi-criteria decision-making based on Pythagorean cubic fuzzy Einstein aggregation operators for investment management

Esmail Hassan Abdullatif Al-Sabri; Muhammad Rahim; Fazli Amin; Rashad Ismail; Salma Khan; Agaeb Mahal Alanzi; Hamiden Abd El-Wahed Khalifa; Esmail Hassan Abdullatif Al-Sabri; Muhammad Rahim; Fazli Amin; Rashad Ismail; Salma Khan; Agaeb Mahal Alanzi; Hamiden Abd El-Wahed Khalifa

doi:10.3934/math.2023866

AIMS Mathematics

2023, Volume 8, Issue 7: 16961-16988. doi: 10.3934/math.2023866

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Multi-criteria decision-making based on Pythagorean cubic fuzzy Einstein aggregation operators for investment management

1.
Department of Mathematics, Faculty of Science and Arts, Mahayl Assir, King Khalid University, Abha, Saudi Arabia
2.
Department of Mathematics and Statistics, Hazara University Mansehra 21300, Khyber Pakhtunkhwa, Pakistan
3.
Department of Mathematics, College of Science and Arts, Qassim University, Al-Badaya 51951, Saudi Arabia
4.
Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Received: 05 March 2023 Revised: 15 April 2023 Accepted: 24 April 2023 Published: 16 May 2023
MSC : 03E72, 08A72, 03B52, 91B0

Pythagorean cubic fuzzy sets (PCFSs) are a more advanced version of interval-valued Pythagorean fuzzy sets where membership and non-membership are depicted using cubic sets. These sets offer a greater amount of data to handle uncertainties in the information. However, there has been no previous research on the use of Einstein operations for aggregating PCFSs. This study proposes two new aggregator operators, namely, Pythagorean cubic fuzzy Einstein weighted averaging (PCFEWA) and Pythagorean cubic fuzzy Einstein ordered weighted averaging (PCFEOWA), which extend the concept of Einstein operators to PCFSs. These operators offer a more effective and precise way of aggregating Pythagorean cubic fuzzy information, especially in decision-making scenarios involving multiple criteria and expert opinions. To illustrate the practical implementation of this approach, we apply an established MCDM model and conduct a case study aimed at identifying the optimal investment market. This case study enables the evaluation and validation of the established MCDM model's effectiveness and reliability, thus making a valuable contribution to the field of investment analysis and decision-making. The study systematically compares the proposed approach with existing methods and demonstrates its superiority in terms of validity, practicality and effectiveness. Ultimately, this paper contributes to the ongoing development of sophisticated techniques for modeling and analyzing complex systems, offering practical solutions to real-world decision-making problems.
- Einstein aggregation operators,
- Pythagorean cubic fuzzy sets,
- decision making,
- investment management,
- Mathematics Subject Classification: 03E72,
- 08A72,
- 03B52,
- 91B0
Citation: Esmail Hassan Abdullatif Al-Sabri, Muhammad Rahim, Fazli Amin, Rashad Ismail, Salma Khan, Agaeb Mahal Alanzi, Hamiden Abd El-Wahed Khalifa. Multi-criteria decision-making based on Pythagorean cubic fuzzy Einstein aggregation operators for investment management[J]. AIMS Mathematics, 2023, 8(7): 16961-16988. doi: 10.3934/math.2023866

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Abstract

Pythagorean cubic fuzzy sets (PCFSs) are a more advanced version of interval-valued Pythagorean fuzzy sets where membership and non-membership are depicted using cubic sets. These sets offer a greater amount of data to handle uncertainties in the information. However, there has been no previous research on the use of Einstein operations for aggregating PCFSs. This study proposes two new aggregator operators, namely, Pythagorean cubic fuzzy Einstein weighted averaging (PCFEWA) and Pythagorean cubic fuzzy Einstein ordered weighted averaging (PCFEOWA), which extend the concept of Einstein operators to PCFSs. These operators offer a more effective and precise way of aggregating Pythagorean cubic fuzzy information, especially in decision-making scenarios involving multiple criteria and expert opinions. To illustrate the practical implementation of this approach, we apply an established MCDM model and conduct a case study aimed at identifying the optimal investment market. This case study enables the evaluation and validation of the established MCDM model's effectiveness and reliability, thus making a valuable contribution to the field of investment analysis and decision-making. The study systematically compares the proposed approach with existing methods and demonstrates its superiority in terms of validity, practicality and effectiveness. Ultimately, this paper contributes to the ongoing development of sophisticated techniques for modeling and analyzing complex systems, offering practical solutions to real-world decision-making problems.

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