Complexity

Complexity / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 5534381 | https://doi.org/10.1155/2021/5534381

Muhammad Yahya, Muhammad Naeem, Saleem Abdullah, Muhammad Qiyas, Muhammad Aamir, "A Novel Approach on the Intuitionistic Fuzzy Rough Frank Aggregation Operator-Based EDAS Method for Multicriteria Group Decision-Making", Complexity, vol. 2021, Article ID 5534381, 24 pages, 2021. https://doi.org/10.1155/2021/5534381

A Novel Approach on the Intuitionistic Fuzzy Rough Frank Aggregation Operator-Based EDAS Method for Multicriteria Group Decision-Making

Academic Editor: Atila Bueno
Received10 Feb 2021
Revised01 Apr 2021
Accepted12 Apr 2021
Published03 Jun 2021

Abstract

The basic ideas of rough sets and intuitionistic fuzzy sets (IFSs) are precise statistical instruments that can handle vague knowledge easily. The EDAS (evaluation based on distance from average solution) approach plays an important role in decision-making issues, particularly when multicriteria group decision-making (MCGDM) issues have more competing criteria. The purpose of this paper is to introduce the intuitionistic fuzzy rough Frank EDAS (IFRF-EDAS) methodology based on IF rough averaging and geometric aggregation operators. We proposed various aggregation operators such as IF rough Frank weighted averaging (IFRFWA), IF rough Frank ordered weighted averaging (IFRFOWA), IF rough Frank hybrid averaging (IFRFHA), IF rough Frank weighted geometric (IFRFWG), IF rough Frank ordered weighted geometric (IFRFOWG), and IF rough Frank hybrid geometric (IFRFHG) on the basis of Frank t-norm and Frank t-conorm. Information is given for the basic favorable features of the analyzed operator. For the suggested operators, a new score and precision functions are described. Then, using the suggested method, the IFRF-EDAS method for MCGDM and its stepwise methodology are shown. After this, a numerical example is given for the established model, and a comparative analysis is generally articulated for the investigated models with some previous techniques, showing that the investigated models are much more efficient and useful than the previous techniques.

1. Introduction

The difficulty of decision-making (DM) issues is increasing with the difficulty of the social and economic surroundings in this competitive world. It is therefore more impossible for a small-decision specialist to achieve an effective and smart decision in this case. In reality, the use of group DM models heavily requires fusing the view of a team of experienced scholars to obtain more reasonable and desired objectives. In addition, in order to achieve more reasonable and sensible DM results, the major value and systemic approach of multicriteria group decision-making (MCGDM) are to increase and evaluate various different criteria in all areas of DM. The knowledge base about such a fact is usually unique in DM issues, and this ambiguity enables the decision process to be difficult and complicated.

Zadeh [1] examined the popular sort of fuzzy sets to deal with this inaccurate knowledge correctly in order to deal with this weakness. A membership degree (MD) is represented by fuzzy set knowledge, and its membership rating is limited to [0, 1], but after the development of this theory, with both theoretical and practical knowledge, it was increasingly investigated in various directions. The popular definition of the intuitionistic fuzzy set (IFS) characterized by two MD and non-MD functions was then examined by Atanassov [2]. At IFS, the sum of MD and non-MD values is limited to the interval [0, 1]. It is now a popular focus of this study for scholars from the beginning of the IFS, and its hybrid impact is explored in various directions. Xu [3] was the developer of the IF weighted average (IFWA) aggregation operator. Xu and Yager [4] represented the notion of IF weighted geometric (IFWG) operators. Ali et al. [5] established the statistical tools for the rating precision and score function. A detailed study of IF neutral averaging operators was conducted by He et al. [6]. He et al. [7] proposed the concept of the average factor of geometric action and suggested its application in DM. The concept of generalized IFWA, the generalized IFOWA operator, and the generalized IFHA operator was started by Zhao et al. [8] and implemented to DM. When using Einstein standard concept, various averages and geometric operators were explained by Wang and Liu [9, 10]. The definition of IFDWA/G intuitionistic fuzzy Dombi weighting average and geometric operators was established by Seikh and Mandal [11]. On the basis of Hamacher t-norm and t-conorm, Huang [12] proposed various aggregation operators. On the basis of Archimedean t-norm and t-conorm, Xia et al. [13] presented various aggregation operators. In addition to just getting three different forms of operators, such as quasi-IF ordered weighted averaging (OWA), quasi-IF Choquet order averaging, and quasi-IFOWA operator based on Dempster–Shafer belief structure, Yang and Chen [14] generalized the idea of the averaging operator. While using IF knowledge, Szmidt and Kacprzyk [15] introduced the principle of entropy calculation. The normative concept of entropy was developed by Hung and Yang [16] on the basis of the concept of IFS probability. Using the interval-valued IF set, the similarity measure of entropy was proposed by We et al. [17]. Through using IF data, the sine and cosine similarity measure and its applications were studied by Ye [18]. IFS was already commonly applied by scholars [19, 20].

The leader who studied the main definition of rough set theory is Pawlak [21]. The classical set theory that works with incorrect and vague information has been extended by this concept. Study of the rough set has made considerable strides in both real applications and the theory on its own in past years. The idea of rough set theory has been expanded by several researchers in different ways. The definition of the fuzzy rough set was generated by Dubois and Prade [22] by introducing the fuzzy connection rather than the crisp discrete connection. The hybrid definition of IFS and rough set plays a key role in studying such various concepts, and the combined IF rough set analysis was created by Cornelis et al. [23]. By introducing IFR approximation operators, Zhou and Wu [24] established constrictive and self-evident analysis. The concept of rough IFS and IFRS was introduced by Zhou and Wu [25], and their constraining and self-evident study in terminology was represented by using the fuzzy rough approximation space theory. The notion of the IF link was established by Bustince and Burillo [26]. The essential structure of IFRS was explored by Zhang et al. [27] after using fundamental IF relationships on the premise of the concept of two universes. Some features of the IFR estimation operator based on the IF relationship were established by Yun and Lee [28] via topology. Various IFRS extensions are examined; for further information, see [2932]. The IF rough soft set, fuzzy soft set approximation space, and its application were proposed by Zhang et al. [33]. Furthermore, the IF covering using the IFRS was proposed by Zhang [34]. The IF soft relation has been developed by Zhang et al. [35]. Using the concept of Pythagorean orthopair fuzzy soft set and rough set, Hussain et al. [3638] discussed their basic properties. Wang and Li [39], using Pythagorean fuzzy information, established the notion of the interaction power Bonferroni mean operator. Wang et al. [4042] analyzed several aggregation operators using only the trapezoidal IF [4347], analyzed different operators of aggregation, and presented their group decision-making frameworks. Wan et al. [47, 48, 49] proposed some aggregation operators on triangular intuitionistic fuzzy numbers.

The developer who researched the EDAS approach for solving DM issues was Ghorabaee et al. [50]. This approach played an important role in DM problem, particularly when there are more conflicting criteria on MCGDM issues. Conventional DM methods such as TOPSIS and VIKOR are the most important techniques to calculate the distance from PIS and NIS. Smallest distance from PIS and the furthest distance with NIS was the best choice. Wei suggested the approach of grey relationship analysis (GRA) for MADM in the IF setting. Even so, the object of the EDAS method was used to find and choose the best results from multiple alternatives using PDAS (positive average solution distance) and NDAS (negative average solution distance), as well as average solution (AVS). The variance across each solution and the AVS is indicated by these two steps. The strongest one must have a higher PDAS score and an inferior NDAS score. The IF-EDAS method was implemented by Ghorabaee et al. [50]. The picture fuzzy weighted averaging/geometric operator was introduced by Zhang et al. [51] and the EDAS method for MCGDM was studied. The neutrosophic soft decision method with a similarity measure and the EDAS method was established by Peng and Liu [52]. Feng et al. [53] suggested adding hesitant fuzzy knowledge of the EDAS methodology. The hybrid operator was proposed by Li et al. [54], and its implementation in DM was analyzed using the EDAS system. Liang [55] applied the EDAS system analysis to the IF area and introduced the energy efficiency project for use. Through using IF knowledge, Kahraman [56] extended the EDAS method to project planning. Ilieva [57] used the interval fuzzy information to present the definition of the EDAS system. For the interval-valued neutrosophic setting, Karasan and Kahraman [57, 58] developed the EDAS approach. To explain the EDAS process, Stanujkic et al. [59] have been using the grey number principle. The definition of elastic fuzzy logic for MCGDM based on the EDAS method was proposed by Keshavarz-Ghorabaee et al. [60]. Stevic et al. [61] proposed using fuzzy knowledge in the EDAS approach for the DM strategy. Ghorabaee et al. [62] gave the idea about the rank reversal process to study and join the EDAS and TOPISIS approaches [63].

Zhang et al. [64] developed t-conorm and t-norm and offered more versatility than most other t-conorms and t-norms. We expand Frank t-conorm and t-norm to intuitionistic fuzzy numbers (IFNs) in this article and define IFN Frank operation laws. Intuitionistic fuzzy numbers (IFNs) containing two areas, the scale of membership degrees and the range of nonmembership degrees, are quite useful for using the representation of fuzzy data. With the assistance of Frank processes, we research the aggregation strategies of IFNs in this article. Initially, we apply the Frank t-conorm and t-norm to intuitionistic fuzzy conditions and implement many modern IFN operations, such as Frank number, Frank product, Frank scalar multiplication, and Frank exponentiation, depending on which we create numerous more intuitionistic fuzzy aggregation operators, as well as the intuitionistic fuzzy rough Frank weighted average operator. Furthermore, we define different characteristics of these operators, give some specific circumstances of them, and examine the relationships among them. In addition, we use the developed concept to build a method with [64] intuitionistic fuzzy data to work with MCDM problems. At the same time, certain moral virtues of these operations are examined, such as idempotency, commutativity, and limitation, and some specific scenarios are analyzed. In addition, Xing et al. [65] addressed several basic organizational laws and objects. Some aggregation operators were explained by Qin et al. [66]. Utilizing Frank t-norm and t-conorm, few general features of the type-2 fuzzy set were also clarified by Qin and Liu [67].

In this article, in Section 3, we present the idea of the new score function and accuracy function for the IFR values (IFRV). In Sections 4 and 5, we also propose the idea of average and geometric aggregation operators such as IFRFWA, IFRFOWA, IFRFHA, IFRFWG, IFRFOWG, and IFRFHG. We introduced the IFR-EDAS framework for MCGDM in Section 6 and illustrated its step-by-step algorithm using the proposed technique. After this, a numerical model focusing on the EDAS method for the choice of the appropriate small hydropower plant (SHPP) from various geographical places in Pakistan is given in Section 7. In addition, a comparative analysis of the proposed method with some previous techniques is widely articulated, showing that the model being examined is more efficient than the previous techniques.

2. Basic Concepts and Definitions

In this portion, some fundamental notions about the FS and IFS operators are given, which are used in our study.

Definition 1. (see [1]). Let be a nonempty given set. A FS in is an object and represented by the mathematical equation as follows:

Definition 2. (see [2]). Let . Then, the IFS is defined aswhere are the grade of positive function of in and are the grade of negative function of in . Also, , , is called the grade of refusal. The pair is called the intuitionistic fuzzy number (IFN). And the condition satisfies .

Definition 3. (see [4]). Let and be IFVs; here, we have some operational laws as follows:

Definition 4. (see [24]). Let us have a fixed set and be a crisp relation. Then,(1) is reflexive if (2) is symmetric if , then (3) is transitive if , , and , then

Definition 5. (see [24]). Let us have a fixed set and for any subsets . We defined a mapping as follows:Here, denotes the successor neighborhood with respect to . The crisp approximation space is represented by a pair . The upper and lower approximations w.r.t approximation space , i.e., , are defined aswhere is known as the rough set and are the upper and lower approximation operators.

Definition 6. (see [24]). Let us have a fixed set and subset ; an IF relation is defined as follows:(1) is reflexive if and (2) is symmetric if and (3) is transitive if , and

2.1. Intuitionistic Fuzzy Rough Set

In this portion, we have developed the score and accuracy function on the basis of the IFS and IF Frank rough set. Furthermore, we will propose some basic operational laws.

Definition 7. (see [68]). Consider to be the universal set, and for any subset , we can define an IF relation. On the basis of pair , we can define the upper and lower approximation which is denoted by and .wheresuch that and . The pair is known to be the IFR set, where and are said to be the IFR upper and lower approximation, respectively. Clearly,

Definition 8. Let be three IFREs, , and . Then, we define the following operational laws:(1).(2).(3).(4).

Definition 9. (see [68]). Let be a IFRS. The score value and accuracy values of IFRS can be evaluated as follows:

Definition 10. (see [68]). Let and be two IFRSs. Then,(1)If , then (2)If , then the accuracy function is compared as(a)If , then (c)If , then

3. Intuitionistic Fuzzy Rough Frank Averaging Aggregation Operator

On the basis of Frank t-norm and Frank t-conorm, we can develop the aggregation operators and take the IFNs and rough sets. We also explained some basic operational laws.

Definition 11. Let be a set of IFRSs in . An IFRFWA operator of dimension is given bywhere the weight vector of is , with and .
On the basis of intuitionistic fuzzy laws of IFNs, the IFRFWA operator can be converted into the shape given in the following by induction on . The aggregated value of by the use of the IFRFWA operator is again an IFRS and can be written as

Theorem 1. Let be IFRSs having weight as . Then, we can define the IFRFWA operator as

Proof. By using mathematical induction,LetLet ; then,Here, we have to prove it is true for , and furthermore, it is checked for .Furthermore, we check for , and we haveThus, the required result holds for . Hence, the required result is true for all .
The basic concepts of upper approximation and lower approximation are IFRVs. So, by Definition 6, and are also IFRSs. Therefore, become IFRS under approximation space .
The IFRFWA operators satisfy the following properties:(1)Idempotency: . So,Then,(2)Boundedness: if and , then(3)Monotonicity: let, be another collection of IFRSs such that and .Then,(4)Shift invariance: let be IFRSs.(5)Homogeneity: for any real number ,(6)Commutativity: let , be any permutation of . Then,

Proof. (1)We have for all , whereFor all , . Therefore,Hence,(2)We have. To prove that, we haveHence,Next, for all , we haveAlso, we proveso from equations (31) to (36), we havewhich is the required proof.(3)Since and are IFRVs, we shall prove the following. To show that and . So, Next,Also, we have to proveHence, from equations (38) to (43), we getTherefore,(4)As is an IFRS, is the family of IFRSs, soTherefore,hence the proof.(5)Let and be an IFRV.Asnow,