LAPSE:2023.22941v1
Published Article

LAPSE:2023.22941v1
A Robust q-Rung Orthopair Fuzzy Information Aggregation Using Einstein Operations with Application to Sustainable Energy Planning Decision Management
March 24, 2023
Abstract
A q-rung orthopair fuzzy set (q-ROFS), an extension of the Pythagorean fuzzy set (PFS) and intuitionistic fuzzy set (IFS), is very helpful in representing vague information that occurs in real-world circumstances. The intention of this article is to introduce several aggregation operators in the framework of q-rung orthopair fuzzy numbers (q-ROFNs). The key feature of q-ROFNs is to deal with the situation when the sum of the qth powers of membership and non-membership grades of each alternative in the universe is less than one. The Einstein operators with their operational laws have excellent flexibility. Due to the flexible nature of these Einstein operational laws, we introduce the q-rung orthopair fuzzy Einstein weighted averaging (q-ROFEWA) operator, q-rung orthopair fuzzy Einstein ordered weighted averaging (q-ROFEOWA) operator, q-rung orthopair fuzzy Einstein weighted geometric (q-ROFEWG) operator, and q-rung orthopair fuzzy Einstein ordered weighted geometric (q-ROFEOWG) operator. We discuss certain properties of these operators, inclusive of their ability that the aggregated value of a set of q-ROFNs is a unique q-ROFN. By utilizing the proposed Einstein operators, this article describes a robust multi-criteria decision making (MCDM) technique for solving real-world problems. Finally, a numerical example related to integrated energy modeling and sustainable energy planning is presented to justify the validity and feasibility of the proposed technique.
A q-rung orthopair fuzzy set (q-ROFS), an extension of the Pythagorean fuzzy set (PFS) and intuitionistic fuzzy set (IFS), is very helpful in representing vague information that occurs in real-world circumstances. The intention of this article is to introduce several aggregation operators in the framework of q-rung orthopair fuzzy numbers (q-ROFNs). The key feature of q-ROFNs is to deal with the situation when the sum of the qth powers of membership and non-membership grades of each alternative in the universe is less than one. The Einstein operators with their operational laws have excellent flexibility. Due to the flexible nature of these Einstein operational laws, we introduce the q-rung orthopair fuzzy Einstein weighted averaging (q-ROFEWA) operator, q-rung orthopair fuzzy Einstein ordered weighted averaging (q-ROFEOWA) operator, q-rung orthopair fuzzy Einstein weighted geometric (q-ROFEWG) operator, and q-rung orthopair fuzzy Einstein ordered weighted geometric (q-ROFEOWG) operator. We discuss certain properties of these operators, inclusive of their ability that the aggregated value of a set of q-ROFNs is a unique q-ROFN. By utilizing the proposed Einstein operators, this article describes a robust multi-criteria decision making (MCDM) technique for solving real-world problems. Finally, a numerical example related to integrated energy modeling and sustainable energy planning is presented to justify the validity and feasibility of the proposed technique.
Record ID
Keywords
aggregation operators, Einstein norms, q-rung orthopair fuzzy numbers, sustainable planning decision management
Subject
Suggested Citation
Riaz M, Sałabun W, Athar Farid HM, Ali N, Wątróbski J. A Robust q-Rung Orthopair Fuzzy Information Aggregation Using Einstein Operations with Application to Sustainable Energy Planning Decision Management. (2023). LAPSE:2023.22941v1
Author Affiliations
Riaz M: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan [ORCID]
Sałabun W: Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 [ORCID]
Athar Farid HM: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Ali N: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Wątróbski J: Department of Information Systems Engineering, Faculty of Economics, Finance and Management University of Szczecin, Mickiewicza 64, 71-101 Szczecin, Poland [ORCID]
Sałabun W: Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 [ORCID]
Athar Farid HM: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Ali N: Department of Mathematics, University of the Punjab, Lahore 54590, Pakistan
Wątróbski J: Department of Information Systems Engineering, Faculty of Economics, Finance and Management University of Szczecin, Mickiewicza 64, 71-101 Szczecin, Poland [ORCID]
Journal Name
Energies
Volume
13
Issue
9
Article Number
E2155
Year
2020
Publication Date
2020-05-01
ISSN
1996-1073
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Original Submission
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PII: en13092155, Publication Type: Journal Article
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LAPSE:2023.22941v1
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Mar 24, 2023
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