Advances in Numerical Optimisation: Theory, Models, and Applications
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Harish Garg, Gulfam Shahzadi, Muhammad Akram, "DecisionMaking Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID19 Testing Facility", Mathematical Problems in Engineering, vol. 2020, Article ID 7279027, 16 pages, 2020. https://doi.org/10.1155/2020/7279027
DecisionMaking Analysis Based on Fermatean Fuzzy Yager Aggregation Operators with Application in COVID19 Testing Facility
Abstract
This research article is devoted to establish some general aggregation operators, based on Yager’s tnorm and tconorm, to cumulate the Fermatean fuzzy data in decisionmaking 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 decisionmaking 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 COVID19 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 decisionmaking results is also examined.
1. Introduction
Decision making (DM) plays a vital role in the practical life activities of human beings as it refers to a process that lays out all the options according to the assessment data of the decision makers and then selects the excellent one, mostly happening in our everyday lives. In the early era of social development, decision makers utilized the real numbers as a rule to offer their assessment information. As the multiattribute decisionmaking (MADM) problems are becoming complex, the experts cannot give exact real numbers to assess the alternatives. The ambiguities and imprecision of human judgments highlighted the deficiency of the crisp set theory. Therefore, Zadeh [1] laid the foundations of the fuzzy set (FS) theory for uncertain knowledge that permits the experts to describe their satisfaction level (membership degree) regarding performance of a member within the unit interval. The FSs provide the grounds to the uncertain assessments, but they were not adequate enough to describe the NMD. To overcome this deficiency of FSs, Atanassov [2] developed intuitionistic fuzzy sets (IFSs) which have both degrees of satisfaction and dissatisfaction that make them more superior and efficient than the traditional FSs. The IFSs accommodate more amount of vagueness in DM as they provide information both in favor (membership grade) and against (nonmembership grade) of the available alternatives. Along with all these advantages, there were some limitations is Atanassov’s model as the sum of MD and NMD is restricted to unit interval. For example, if MD of an element in a set is 0.7 and NMD is 0.4, then sum of these values is greater than 1. To handle such situations, Yager [3] established Pythagorean fuzzy sets (PFSs) with relaxed conditions that enable them to handle imprecise decisions efficiently. However, the PFS also has some limitations; if MD of an element in a set is 0.9 and NMD is 0.6, then sum of square of these values is greater than 1. Then, Yager [4] developed the theory of the rung orthopair fuzzy set (ROFS) with condition that the sum of power of MD and NMD is bounded by 1. Recently, Senapati and Yager [5] gave the concept of FFS as a generalization of IFS and PFS.
The concept of aggregation operators (AOs) was introduced to get a unique value by the list of values. The theory of averaging operators under intuitionistic fuzzy (IF) environment was studied by Xu [6]. Xu and Yager [7] discussed geometric operators under IF environment. Li [8] studied the generalized ordered weighted averaging operators for IF data. The idea of induced geometric AOs under IF information was proposed by Wei [9]. The arithmetic and geometric operators were discussed in detail under Pythagorean fuzzy (PF) environment by Yager [10]. Peng and Yang [11] developed the fundamental properties of intervalvalued PF aggregation operators. Zeng et al. [12] proposed a hybrid structure for PF MADM. 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 decisionmaking approaches with explanatory examples for the PF information. Many researchers gave more attention to the MADM based on the aggregation operators under different models of obscure knowledge, including IFS and PFS, [17–25]. Liu and Wang [26] discussed ROF weighted AOs. Dombi AOs under ROFS were defined by Jana et al. [27]. Liu et al. [28] studied ROF power Maclaurin symmetric mean operators. Liu and Liu [29] discussed ROF Bonferroni mean operators. Senapati and Yager [30] studied subtraction, division, and Fermatean arithmetic mean operations over FFS. The idea of Fermatean fuzzy (FF) weighted averaging/geometric operators was also given by Senapati and Yager [31]. For other terminologies not discussed in the paper, the readers are referred to [32–39]. There are some incentives of this article:(1)The judgment of perfect alternative is a difficult task under MADM environment when assessment data are simply illustrated by IF numbers and PF numbers which may prompt data mutilation. Therefore, we need a more general model to elaborate the potential of alternatives.(2)Fermatean fuzzy sets, a remarkable extension of IFSs and PFSs, permit modeling of situations with more generality than IFSs and PFSs because these previous models fail to handle the situations where .(3)As a prevalent set, Fermatean fuzzy numbers (FFNs) indicate extraordinary execution in providing vague, reliable, and inexact assessment information due to the modified and relaxed conditions. Therefore, FFNs might be the best approach for assessing the potential of alternatives.(4)Yager AOs are straight forward, however, groundbreaking, approach for solving DM problems. Therefore, in this article, we aim to define Yager AOs in the FF context to deal with difficult MADM problems of choice.(5)Yager AOs exhibit more precise results when applied to reallife MADM based on the FF data.(6)The drawbacks and limitations of the existing operators are run over by the proposed operators as these operators are more general that work excellently not only for FF information but also for IF and PF data.
The major contributions of this research are as follows:(1)The theory of Yager AOs is extended to FF numbers, and a thorough discussion is presented to analyze the important results and dominant properties of the proposed operators.(2)An algorithm is proposed to deal complex practical MADM problems with FF data. The proposed algorithm is supported by an illustrative example for the selection of the most authentic lab for the COVID19 test.(3)The impact of the parameter’s values on the proposed operators is explored to verify their authenticity.(4)The consistency of the proposed approach is checked by conducting a comparative analysis with the existing operators and the FF TOPSIS method.
The remaining paper is composed as follows: Section 2 recalls the elementary notions of the FF and other existing models. In Section 3, we present Yager operations for Fermatean fuzzy numbers (FFNs) and establish some laws and aggregation operators. Section 4 gives an algorithm for MADM and discuss a numerical example in the field of medical based on FFNs. In Section 5, results are compared with existing operators to show the superiority and validity of the proposed theory. We have presented the comparison between the proposed theory and the FF TOPSIS method. Section 6 provides the conclusions about the proposed theory.
2. Preliminaries
In this section, we review some basic concepts on a fuzzy set.
Definition 1 (see [2]). An intuitionistic fuzzy set over the domain is defined aswhere and specify MD and NMD of an element , respectively. For an element , represents the indeterminacy degree (InD).
Definition 2 (see [3]). A Pythagorean fuzzy set over the domain is defined aswhere and specify MD and NMD of every element, respectively. is InD.
Definition 3 (see [5]). A Fermatean fuzzy set over the domain is defined aswhere , , and specify MD, NMD, and InD, respectively. FFNs are components of the FFS.
Definition 4 (see [5]). The score function and accuracy function for FFN are represented by
Definition 5 (see [5]). Consider two FFNs and . Then,(1)If then (2)If then (3)If then(a)If then (b)If then (c)If then
3. Fermatean Fuzzy Numbers under Yager Operations
This section addresses some operational laws and their operators for FFNs.
3.1. Operational Laws for FFNs
Definition 6. Let and be two FFNs, and . Then, Yager norm and conorm operations of FFNs are(1)(2)(3)(4)
Example 1. Let and be two FFNs, and then by using Definition 6 for they are:
Theorem 1. Let be three FFNs, and then(1)(2)(3)(4)(5)(6)
Proof. For three FFNs and , by Definition 6,In this same way, other properties can be done.
3.2. Fermatean Fuzzy Yager Hybrid Weighted Arithmetic Operators
Yager weighted arithmetic operators under FF environment are discussed here.
Definition 7. Let be a collection of FFNs. The FFYWA operator is a function s.t.where is the weight vector (WV) of with and .
Theorem 2. Let be a collection of FFNs, and then the aggregated value of them by the FFYWA operation is an FFN and
Proof. To prove this result, use mathematical induction.(i)When , Therefore, Hence, equation (8) is true for .(ii)Let equation (8) holds for ,Now, for ,Hence, equation (8) is true for . Thus, equation (8) is true, .
Example 2. Let , and be FFNs with a WV and . By Theorem 2, the aggregated value of FFNs is
Theorem 3 (idempotency). If all FFNs are identical, i.e., , then
Proof.
Theorem 4 (boundedness). Let be a collection of FFNs. Let and . Then,
Proof. Suppose that and , where , , , and . The inequalities for membership value areSimilarly, for nonmembership value,Therefore,.
Theorem 5 (monotonicity). Let and be two collections of FFNs. If and , . Then,
Proof. Let and . First, we will show that . As , . Moreover,Therefore, . Similarly, we can show that . Hence, .
Theorem 6 (reducibility). Let be a collection of FFNs with corresponding WV . Then, the FFYWA operator isWe now define the FFYOWA operators.
Definition 8. Let be a collection of FFNs with WV with and . The FFYOWA operator is a function s.t.where is permutation of s.t. .
Theorem 7. The aggregated value by applying the FFYOWA operator is also an FFN and given by
Example 3. Let , and be FFNs with a WV and . Then,Since , Thus, by applying the FFYOWA operator,We give some statements without proofs.
Theorem 8 (idempotency). If all FFNs are identical, i.e., , then
Theorem 9 (boundedness). Let be a collection of FFNs. Let and . Then,
Theorem 10 (monotonicity). Let and be two collections of FFNs. If and , . Then,
Theorem 11. Let be a collection of FFNs with corresponding WV . Then, the FFYOWA operator is
We now define the FFHWA operators.
Definition 9. A is a function , with correlated WV with and s.t.where is the biggest weighted Fermatean fuzzy values and is the balancing coefficient.
Remark 1. For , FFYWA and FFYOWA operators are a particular example of the FFYHWA operator. Thus, the FFYHWA operator is a generalization of both operators.
3.3. Fermatean Fuzzy Yager Hybrid Weighted Geometric Operators
Yager weighted geometric operators are discussed here for FF data.
Definition 10. Let be a number of FFNs. The FFYWG operator is a function s.t.where is WV of with and .
Theorem 12. The aggregated value by applying the FFYWG operator is also an FFN and given by
Proof. It is similar to Theorem 2.
Example 4. Consider Example 2, and by Theorem 12, the aggregated value for FFNs isWe give some statements without the proof.
Theorem 13 (idempotency). If all FFNs are identical, i.e., , then
Theorem 14 (boundedness). Let be a number of FFNs. Let and . Then
Theorem 15 (monotonicity). Consider two collections and of FFNs. If and , . Then,
Theorem 16 (reducibility). Let be a collection of FFNs with WV = . Then, FFYWG operator is
We now define FFYOWG operators.
Definition 11. Let be a collection of FFNs with WV with and . The FFYOWG operator is a function s.t.where is permutation of s.t. .
Theorem 17. The aggregated value by applying the FFYOWG operator is also an FFN and given by
Proof. It is similar to Theorem 2.
Example 5. Consider Example 3, and by Theorem 17, the clumped value for FFNs is
Remark 2. FFYOWG operators satisfy the properties (31), (33), (40), and (42).
Now, we define FFYHWG operators.
Definition 12. An FFYHWG operator is a function , with correlated WV with and s.t.where is the biggest weighted Fermatean fuzzy values .
4. MADM Problems under Proposed Operators
In this section, based on the FFYWA (or FFYWG) operator, we propose an MADM problem with FFNs. Let be a set of alternatives and is WV of the attributes , where and . Suppose that is the FF decision matrix, where and are satisfaction and dissatisfaction degrees of the alternative for the attribute given by the decision maker (DMr), where .
For solving an MADM problem, Algorithm 1 is given as follows.
