A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM
Abstract
:1. Introduction
2. Preliminaries
- (1)
- If , then
- (2)
- If , then
- if then ,
- if , then .
2.1. The Study’s Motivation and Intense Focus
- This article covers two main issues: the theoretical model of the problem and the application of decision-making.
- The proposed models of aggregated operators are credible, valid, versatile and better than the rest to others because they will be based on the generalized q-ROFN structure. If the suggested operators are used in the context of IFNs or PFNs, the results will be ambiguous leading to the decrease of information in the inputs. This loss is due to restrictions on membership and non-membership of IFNs and PFNs. (see Figure 1). The IFNs and PFNs become special cases of q-ROFNs when and respectively.
- The main objective is to establish strong relationships with the multi-criteria decision-making issues between the proposed operators. The application shall communicate the effectiveness, interpretation and motivation of the proposed aggregated operators.
- This research fills the research gap and provides us a wide domain for the input data selection in medical, business, artificial intelligence, agriculture, and engineering. We can tackle those problems which contain ambiguity and uncertainty due to its limitations. The results obtained by using proposed operators and q-ROFNs will be superior and profitable in decision-making techniques.
- (i)
- (ii)
- (iii)
- (iv)
- (v)
- (vi)
- (vii)
- (viii)
2.2. Superiority and Comparison of q-ROFNs with Some Existing Theories
3. q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators
3.1. q-ROFEPWA Operator
3.2. q-ROFEPWG Operator
4. Proposed Methodology
Algorithm 1 |
Step 1: |
Acquire a decision matrix in the form of q-ROFNs from the decision makers.
|
Step 2: |
Two types of criteria are specified in the decision matrix, namely cost type criteria and benefit type criteria . If all Criterions are the same type, there is no need for normalization, but there are two types of Criterions in MCGDM. In this case, using the normalization formula Equation (44) the matrix has been changed into transformed response matrix |
Step 3: |
Calculate the values of by following formula.
|
Step 4: |
Use one of the suggested aggregation operators.
|
or
|
To aggregate all individual q-ROF decision matrices into one cumulative assessments matrix of the alternatives |
Step 5: |
Calculate the values of by the following formula.
|
Step 6: |
Aggregate the q-ROF values for each alternative by the q-ROFEPWA (or q-ROFEPWG) operator: |
or
|
Step 7: |
Evaluate the score of the all cumulative alternative assessments. |
Step 8: |
Ranked the alternatives by the score function and ultimately choose the most appropriate alternative. |
5. Illustrative Example
Comparison Analysis
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Set Theory | Truth Information | Falsity Information | Advantages | Limitations |
---|---|---|---|---|
Fuzzy sets [1] | 🗸 | × | can handle uncertainty using fuzzy interval | do not give any information about the NMD in input data |
Intuitionistic Fuzzy sets [2] | 🗸 | 🗸 | can handle uncertainty using MD and NMD | cannot deal with the problems satisfying |
Pythagorean Fuzzy sets [4,5] | 🗸 | 🗸 | larger valuation space than IFNs | cannot deal with the problems satisfying |
q-rung orthopair Fuzzy sets [52] | 🗸 | 🗸 | larger valuation space than IFNs and PFNs | cannot deal with the problems when MD = 1 and NMD = 1 |
Criterions | |
---|---|
Rich portfolios | |
Timely project delivery | |
Goodwill and reputation | |
Quality of construction | |
Credentials | |
Expertise |
(0.90, 0.00) | (0.65, 0.35) | (0.75, 0.15) | (0.95, 0.15) | (0.75, 0.00) | (0.45, 0.25) | |
(0.95, 0.25) | (0.80, 0.30) | (0.55, 0.25) | (0.75, 0.15) | (0.45, 0.45) | (0.35, 0.15) | |
(0.85, 0.15) | (0.35, 0.55) | (0.75, 0.25) | (0.55, 0.00) | (0.65, 0.35) | (0.45, 0.00) | |
(0.75, 0.35) | (0.81, 0.25) | (0.65, 0.15) | (0.35, 0.25) | (0.75, 0.25) | (0.35, 0.75) | |
(0.80, 0.25) | (0.60, 0.00) | (0.25, 0.15) | (0.15, 0.65) | (0.65, 0.15) | (0.25, 0.65) |
(0.75, 0.25) | (0.55, 0.30) | (0.85, 0.15) | (0.95, 0.15) | (0.80, 0.25) | (0.90, 0.15) | |
(0.55, 0.15) | (0.60, 0.35) | (0.45, 0.15) | (0.75, 0.35) | (0.65, 0.30) | (0.75, 0.00) | |
(0.90, 0.60) | (0.65, 0.20) | (0.25, 0.55) | (0.65, 0.55) | (0.15, 0.25) | (0.70, 0.30) | |
(0.50, 0.00) | (0.55, 0.40) | (0.15, 0.10) | (0.50, 0.60) | (0.10, 0.15) | (0.60, 0.35) | |
(0.85, 0.35) | (0.70, 0.30) | (0.65, 0.55) | (0.25, 0.50) | (0.50, 0.30) | (0.50, 0.25) |
(0.90, 0.15) | (0.85, 0.25) | (0.80, 0.00) | (0.70, 0.35) | (0.80, 0.20) | (0.70, 0.30) | |
(0.80, 0.25) | (0.55, 0.15) | (0.60, 0.25) | (0.50, 0.30) | (0.60, 0.30) | (0.60, 0.30) | |
(0.75, 0.15) | (0.65, 0.25) | (0.35, 0.00) | (0.50, 0.35) | (0.75, 0.30) | (0.35, 0.25) | |
(0.35, 0.35) | (0.50, 0.35) | (0.45, 0.25) | (0.55, 0.45) | (0.25, 0.25) | (0.65, 0.00) | |
(0.65, 0.25) | (0.65, 0.25) | (0.60, 0.15) | (0.65, 0.25) | (0.65, 0.55) | (0.45, 0.40) |
(0.90, 0.00) | (0.35, 0.65) | (0.75, 0.15) | (0.95, 0.15) | (0.75, 0.00) | (0.45, 0.25) | |
(0.95, 0.25) | (0.30, 0.80) | (0.55, 0.25) | (0.75, 0.15) | (0.45, 0.45) | (0.35, 0.15) | |
(0.85, 0.15) | (0.55, 0.35) | (0.75, 0.25) | (0.55, 0.00) | (0.65, 0.35) | (0.45, 0.00) | |
(0.75, 0.35) | (0.25, 0.81) | (0.65, 0.15) | (0.35, 0.25) | (0.75, 0.25) | (0.35, 0.75) | |
(0.80, 0.25) | (0.00, 0.60) | (0.25, 0.15) | (0.15, 0.65) | (0.65, 0.15) | (0.25, 0.65) |
(0.75, 0.25) | (0.30, 0.55) | (0.85, 0.15) | (0.95, 0.15) | (0.80, 0.25) | (0.90, 0.15) | |
(0.55, 0.15) | (0.35, 0.60) | (0.45, 0.15) | (0.75, 0.35) | (0.65, 0.30) | (0.75, 0.00) | |
(0.90, 0.60) | (0.20, 0.65) | (0.25, 0.55) | (0.65, 0.55) | (0.15, 0.25) | (0.70, 0.30) | |
(0.50, 0.00) | (0.40, 0.55) | (0.15, 0.10) | (0.50, 0.60) | (0.10, 0.15) | (0.60, 0.35) | |
(0.85, 0.35) | (0.30, 0.70) | (0.65, 0.55) | (0.25, 0.50) | (0.50, 0.30) | (0.50, 0.25) |
(0.90, 0.15) | (0.25, 0.85) | (0.80, 0.00) | (0.70, 0.35) | (0.80, 0.20) | (0.70, 0.30) | |
(0.80, 0.25) | (0.15, 0.55) | (0.60, 0.25) | (0.50, 0.30) | (0.60, 0.30) | (0.60, 0.30) | |
(0.75, 0.15) | (0.25, 0.65) | (0.35, 0.00) | (0.50, 0.35) | (0.75, 0.30) | (0.35, 0.25) | |
(0.35, 0.35) | (0.35, 0.50) | (0.45, 0.25) | (0.55, 0.45) | (0.25, 0.25) | (0.65, 0.00) | |
(0.65, 0.25) | (0.25, 0.65) | (0.60, 0.15) | (0.65, 0.25) | (0.65, 0.55) | (0.45, 0.40) |
(0.8622, 0.0000) | (0.3303, 0.6444) | (0.7985, 0.0000) | (0.9129, 0.2587) | (0.7792, 0.0000) | (0.7117, 0.2274) | |
(0.8590, 0.2065) | (0.3042, 0.7404) | (0.5334, 0.2139) | (0.7119, 0.1751) | (0.5479, 0.3759) | (0.5720, 0.0000) | |
(0.8510, 0.2408) | (0.4627, 0.4619) | (0.6148, 0.0000) | (0.5780, 0.0000) | (0.5997, 0.3068) | (0.5397, 0.0000) | |
(0.6384, 0.0000) | (0.2981, 0.7340) | (0.5416, 0.1429) | (0.4375, 0.3524) | (0.6023, 0.2099) | (0.4767, 0.0000) | |
(0.7908, 0.2786) | (0.2086, 0.6314) | (0.4966, 0.2182) | (0.3298, 0.5559) | (0.6107, 0.2364) | (0.3797, 0.4849) |
(0.7733, 0.0000) | |
(0.7111, 0.0000) | |
(0.7063, 0.0000) | |
(0.5496, 0.0000) | |
(0.6383, 0.3737) |
Method | Ranking of Alternatives | The Optimal Alternative |
---|---|---|
q-ROFEWA (Riaz et al. [48]) | ||
q-ROFEOWA (Riaz et al. [48]) | ||
q-ROFEWG (Riaz et al. [48]) | ||
q-ROFEOWG (Riaz et al. [48]) | ||
q-ROFWA ( Liu & Wang [58]) | ||
q-ROFWG (Liu & Wang [58]) | ||
q-ROFWBM ( Liu & Liu [59]) | ||
q-ROFWGBM (Liu & Liu [59]) | ||
q-ROFHM ( Zhao et al. [60]) | ||
q-ROFWHM ( Zhao et al. [60]) | ||
q-ROFHM (Liu et al. [61]) | ||
q-ROFWHM (Liu et al. [61]) | ||
q-ROFPHM (Liu et al. [61]) | ||
q-ROFWPHM (Liu et al. [61]) | ||
q-ROFEPWA (Proposed) |
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Riaz, M.; Athar Farid, H.M.; Kalsoom, H.; Pamučar, D.; Chu, Y.-M. A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM. Symmetry 2020, 12, 1058. https://doi.org/10.3390/sym12061058
Riaz M, Athar Farid HM, Kalsoom H, Pamučar D, Chu Y-M. A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM. Symmetry. 2020; 12(6):1058. https://doi.org/10.3390/sym12061058
Chicago/Turabian StyleRiaz, Muhammad, Hafiz Muhammad Athar Farid, Humaira Kalsoom, Dragan Pamučar, and Yu-Ming Chu. 2020. "A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM" Symmetry 12, no. 6: 1058. https://doi.org/10.3390/sym12061058
APA StyleRiaz, M., Athar Farid, H. M., Kalsoom, H., Pamučar, D., & Chu, Y. -M. (2020). A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM. Symmetry, 12(6), 1058. https://doi.org/10.3390/sym12061058