Decision-Making Approach under Pythagorean Fuzzy Yager Weighted Operators

Shahzadi, Gulfam; Akram, Muhammad; Al-Kenani, Ahmad N.

doi:10.3390/math8010070

Cited by 66 publications

(40 citation statements)

References 33 publications

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“…Operators. To compute performance and validity of our proposed operators, here we aggregate the same data using different operators, namely, Pythagorean fuzzy Yager weighted average (PFYWA) [14] and Pythagorean fuzzy Yager weighted geometric (PFYWG) [14] operators. e computed results by applying these operators are summarized in Table 4 and shown in Figure 3.…”

Section: Comparison With Pythagorean Fuzzy Yager Aggregationmentioning

confidence: 99%

“…e main logic behind our proposed approach is that the PFS handles the situation where μ 2 + ] 2 ≤ 1 but fails in situations where μ 3 + ] 3 ≤ 1. If we assign MD 0.8 and NMD 0.7, then the proposed operators in [14] fail to cope the situation. at is why, we need the FFS and our proposed theory.…”

Section: Comparison With Pythagorean Fuzzy Yager Aggregationmentioning

confidence: 99%

“…Akram et al [13] worked for the development of Pythagorean Dombi fuzzy AOs with applications. Shahzadi et al [14] developed the theory of Yager AOs under PF data. Akram et al [15,16] proposed the group decision-making approaches with explanatory examples for the PF information.…”

Section: Introductionmentioning

confidence: 99%

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Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Garg

Shahzadi

Akram

2020

Mathematical Problems in Engineering

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132

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This research article is devoted to establish some general aggregation operators, based on Yager’s t-norm and t-conorm, to cumulate the Fermatean fuzzy data in decision-making environments. The Fermatean fuzzy sets (FFSs), an extension of the orthopair fuzzy sets, are characterized by both membership degree (MD) and nonmembership degree (NMD) that enable them to serve as an excellent tool to represent inexact human opinions in the decision-making process. In this article, the valuable properties of the FFS are merged with the Yager operator to propose six new operators, namely, Fermatean fuzzy Yager weighted average (FFYWA), Fermatean fuzzy Yager ordered weighted average (FFYOWA), Fermatean fuzzy Yager hybrid weighted average (FFYHWA), Fermatean fuzzy Yager weighted geometric (FFYWG), Fermatean fuzzy Yager ordered weighted geometric (FFYOWG), and Fermatean fuzzy Yager hybrid weighted geometric (FFYHWG) operators. A comprehensive discussion is made to elaborate the dominant properties of the proposed operators. To verify the importance of the proposed operators, an MADM strategy is presented along with an application for selecting an authentic lab for the COVID-19 test. The superiorities of the proposed operators and limitations of the existing operators are discussed with the help of a comparative study. Moreover, we have explained comparison between the proposed theory and the Fermatean fuzzy TOPSIS method to check the accuracy and validity of the proposed operators. The influence of various values of the parameter in the Yager operator on decision-making results is also examined.

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Section: Comparison With Pythagorean Fuzzy Yager Aggregationmentioning

confidence: 99%

Section: Comparison With Pythagorean Fuzzy Yager Aggregationmentioning

confidence: 99%

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Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Garg

Shahzadi

Akram

2020

Mathematical Problems in Engineering

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132

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“…Garg (2018b) has also considered Linguistic Pythagorean fuzzy sets, correlation coefficient between Pythagorean fuzzy sets (Garg, 2016) and generalized Pythagorean fuzzy geometric interactive aggregation operators using Einstein operations (Garg, 2018c). Certain decision making methods are discussed by the authors (Shahzadi, Akram and Al‐Kenani, 2020; Waseem, Akram and Alcantud, 2019). Ramot, Milo, Fiedman and Kandel (2002) initiated the idea of complex fuzzy set (CFS) by extending the range of membership from [0,1] to complex numbers within unit disk.…”

Section: Introductionmentioning

confidence: 99%

Complex Pythagorean Dombi fuzzy operators using aggregation operators and their decision‐making

2020

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A complex Pythagorean fuzzy set, an extension of Pythagorean fuzzy set, is a powerful tool to handle two dimension phenomenon. Dombi operators with operational parameters have outstanding flexibility. This article presents certain aggregation operators under complex Pythagorean fuzzy environment, including complex Pythagorean Dombi fuzzy weighted arithmetic averaging (CPDFWAA) operator, complex Pythagorean Dombi fuzzy weighted geometric averaging (CPDFWGA) operator, complex Pythagorean Dombi fuzzy ordered weighted arithmetic averaging (CPDFOWAA) operator and complex Pythagorean Dombi fuzzy ordered weighted geometric averaging (CPDFOWGA) operator. Moreover, this paper explores some fundamental properties of these operators with appropriate elaboration. A decision‐making numerical example related to the selection of bank to purchase loan is given to demonstrate the significance of our proposed approach. Finally, a comparative analysis with existing operators is given to demonstrate the peculiarity of our proposed operators.

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“…Akram et al [18] proposed different Pythagorean Dombi fuzzy AOs and studied their applications in MCDM. Shahzadi et al [19] introduced Pythagorean fuzzy Yager AOs for decision making. Peng and Yang [20] investigated different basic properties of interval-valued Pythagorean fuzzy AOs.…”

Section: Introductionmentioning

confidence: 99%

Extensions of Dombi Aggregation Operators for Decision Making under m-Polar Fuzzy Information

Akram

Yaqoob

Ali

et al. 2020

Journal of Mathematics

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An m-polar fuzzy set is a powerful mathematical model to analyze multipolar, multiattribute, and multi-index data. The m-polar fuzzy sets have appeared as a useful tool to portray uncertainty in multiattribute decision making. The purpose of this article is to analyze the aggregation operators under the m-polar fuzzy environment with the help of Dombi norm operations. In this article, we develop some averaging and geometric aggregation operators using Dombi t-norm and t-conorm to handle uncertainty in m-polar fuzzy (mF, henceforth) information, which are mF Dombi weighted averaging (mFDWA) operator, mF Dombi ordered weighted averaging (mFDOWA) operator, mF Dombi hybrid averaging (mFDHA) operator, mF Dombi weighted geometric (mFDWG) operator, mF Dombi weighted ordered geometric operator, and mF Dombi hybrid geometric (mFDHG) operator. We investigate properties, namely, idempotency, monotonicity, and boundedness, for the proposed operators. Moreover, we give an algorithm to solve multicriteria decision-making issues which involve mF information with mFDWA and mFDWG operators. To prove the validity and feasibility of the proposed model, we solve two numerical examples with our proposed models and give comparison with mF-ELECTRE-I approach (Akram et al. 2019) and mF Hamacher aggregation operators (Waseem et al. 2019). Finally, we check the effectiveness of the developed operators by a validity test.

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Decision-Making Approach under Pythagorean Fuzzy Yager Weighted Operators

Cited by 66 publications

References 33 publications

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Decision-Making Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID-19 Testing Facility

Complex Pythagorean Dombi fuzzy operators using aggregation operators and their decision‐making

Extensions of Dombi Aggregation Operators for Decision Making under m-Polar Fuzzy Information

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