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Circular n,m-rung orthopair fuzzy sets and their applications in multicriteria decision-making

  • Ibtesam Alshammari and Hariwan Z. Ibrahim EMAIL logo
Published/Copyright: February 11, 2025
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Abstract

The circular Pythagorean fuzzy set is an expansion of the circular intuitionistic fuzzy set (CIFS), in which each component is represented by a circle. Nevertheless, even though CIFS improves the intuitionistic fuzzy set representation, it is still restricted to the inflexible intuitionistic fuzzy interpretation triangle (IFIT) space, where the square sum of membership and nonmembership in a circular Pythagorean fuzzy environment and the sum of membership and nonmembership in a circular intuitionistic fuzzy environment cannot exceed one. To overcome this restriction, we provide a fresh extension of the CIFS called the circular n,m-rung orthopair fuzzy set (Cn,m-ROFS), which allows the IFIT region to be expanded or contracted while maintaining the features of CIFS. Consequently, decision makers can assess items over a wider and more flexible range when using a Cn,m-ROFS, allowing for the making of more delicate decisions. In addition, we define several basic algebraic and arithmetic operations on Cn,m-ROFS, such as intersection, union, multiplication, addition, and scalar multiplication, and we discuss their key characteristics together with some of the known relations over Cn,m-ROFS. In addition, we present and study the new circular n,m-rung orthopair fuzzy weighted average/geometric aggregation operators and their properties. Further, a strategy for resolving multicriteria decision-making problems in a Cn,m-ROF environment is provided. The suggested strategy is tested on two situations: the best teacher selection problem and the best school selection problem. To confirm and illustrate the efficacy of the suggested methodology, a comparative analysis with the intuitionistic fuzzy weighted average, intuitionistic fuzzy weighted geometric, q-rung orthopair fuzzy weighted averaging, q-rung orthopair fuzzy geometric averaging, circular PFWA max , and circular PFWA min operators approaches is also carried out. Ultimately, in the final section, there are discussions and ideas for future research.

MSC 2010: 03B52; 90B50

1 Introduction

Decision theory is a rapidly evolving discipline, particularly in the area of multicriteria and multigroup decision-making. These procedures often require a group to rank, select, or assign a set of alternatives. These alternatives are evaluated based on multiple competing criteria. The process incorporates input data from various sources, including both qualitative and quantitative information. However, several factors can lead to issues with the data. These include subjectivity, incomplete knowledge, measurement errors, and other uncertainties. Fuzzy set (FS) theory has been proposed in several decision-making models to address these challenges [1]. In FS, each element of a set is characterized by a membership degree, with a value ranging between [ 0 , 1 ] , complemented by a nonmembership degree. However, FS does not account for hesitation or uncertainty. In real-life scenarios, individuals may struggle to express a clear preference when asked to provide a rating. To address this limitation, Atanassov introduced the intuitionistic fuzzy set (IFS) [2]. IFS extends FS by allowing separate degrees for membership and nonmembership within the unit interval. In addition, it introduces a hesitancy degree, where the sum of the membership and nonmembership degrees is less than one. Despite these improvements, IFS becomes ineffective if the experts provide estimates where the sum exceeds one in certain cases. To overcome this issue, Yager proposed the Pythagorean fuzzy set (PFS) [3]. PFS generalizes IFS by restricting the square sum of membership and nonmembership degrees to one. This innovation has attracted significant interest among researchers due to its enhanced ability to handle uncertainty compared to IFS. PFS has been widely applied in various decision-making scenarios. Nevertheless, questions arose regarding how far theoretical models could go in explaining real-world scenarios. To address these concerns, Yager introduced the q-rung orthopair fuzzy set (q-ROFS) [4]. In q-ROFS, the sum of the qth powers of membership and nonmembership degrees cannot exceed one. This framework generalizes both PFSs and intuitionistic fuzzy sets, further extending their applicability. When q = 3 , Senapati and Yager [5] referred to the q-ROFS as a Fermatean fuzzy set (FFS) and discussed several of its fundamental properties. FFSs serve as an effective human-centered reasoning tool for capturing uncertainty in automated decision-making processes. The key characteristic of FFS is that the cube sum of membership and nonmembership values must be less than or equal to one. This feature makes FFS a more robust approach compared to FS, IFS, and PFS. A newly developed fuzzy set extension called the n,m-rung orthopair fuzzy set (n,m-ROFS) was recently studied by Ibrahim and Alshammari [6]. Its unique feature is that the total of the n th power of an element’s membership and the mth power of its nonmembership values cannot be greater than one. In comparison to q-ROFS, the expression of n,m-ROFS is generally regarded to offer decision-makers more expressive power and flexibility in articulating their preferences. To illustrate this argument more clearly, consider a pair of membership and nonmembership degrees represented as ( 0.89 , 0.90 ) . It becomes evident that 0.8 9 q + 0.9 0 q > 1 when q 6 , which highlights a scenario where using the same exponent q fails to keep the sum within the required bounds. However, by introducing nonsymmetric values for n and m , we can ensure the condition 0.8 9 n + 0.9 0 m < 1 . For instance, this holds true when n > 6 and m = 6 , or n = 6 and m > 6 .

The models of FS, IFS, PFS, FFS, q-ROFS, and n,m-ROFS have been applied to tackle numerous decision-making problems. A finite game with FSs of player tactics is examined by Bekesiene and Mashchenko [7]. Four various methods to classify fresh objects have been provided by Chacón-Gómez et al. [8], each based on a novel concept. They additionally offered a thorough discussion about the benefits and drawbacks of each approach, demonstrating how each one addresses issues with the preceding approach and how they can be mixed and utilized on the same set of information. Liu [9] investigated a fuzzy number data clustering method based on the idea of belief functions. Hussain and Ullah [10] introduced a novel approach utilizing spherical fuzzy environments to better model human opinion in complex real-life applications. They propose the use of Sugeno-Weber aggregation operators, which are enhanced by spherical fuzzy logic to deal with incomplete and redundant information, a challenge often faced in multiattribute decision-making (MADM) problems. Kannan et al. [11] presented a novel decision-making approach by integrating the linear diophantine fuzzy set with the combinative distance-based assessment method to handle uncertainty and imprecision in human judgment. The study applied this hybrid method to the selection of a logistics specialist in emergency logistics optimization. A multicriteria risk-based supplier selection problem under fuzziness is applied by Kahraman et al. [12], who expand the technique of order preference similarity to the ideal solution (TOPSIS) technique to the intuitionistic fuzzy TOPSIS using ordered pairs method. Frank’s operational laws were ascertained by Yang et al. [13] using complex intuitionistic fuzzy data, and they also demonstrated the application of the MADM technique while taking into account the novel approaches to demonstrate the superiority and validity of the derived strategies. Dhankhar and Kumar [14] suggested an enhanced possibility degree measure to rank the intuitionistic fuzzy numbers. A weighted partitioned Maclaurin symmetric mean operator-based multiattribute group decision-making approach for intuitionistic fuzzy numbers was presented by Liu et al. [15]. The modified intuitionistic fuzzy weighted geometric (IFWG) operator of intuitionistic fuzzy values was used by Zou et al. [16] to construct a multiple attribute decision-making technique. Group decision-making using heterogeneous intuitionistic fuzzy preference relations, such as additive linguistic intuitionistic fuzzy preference relations, multiplicative linguistic intuitionistic fuzzy preference relations, multiplicative linguistic intuitionistic fuzzy preference relations, and intuitionistic fuzzy preference relations, was investigated by Meng et al. [17]. Zhang et al. [18] looked at how incomplete intuitionistic multiplicative preference relations affected decision-making. Imran et al. [19] addressed the challenge of robot selection for industrial tasks, emphasizing the complexity arising from the continuous integration of advanced features by suppliers. Their study proposes a hybrid approach combining Aczel-Alsina and Bonferroni mean operators within an interval-valued intuitionistic fuzzy framework. Shahzadi et al. [20] established six families of aggregation operators known as Pythagorean fuzzy Yager weighted geometric/ordered weighted geometric and hybrid weighted geometric aggregations, as well as Pythagorean fuzzy Yager weighted averaging/ordered weighted averaging and hybrid weighted averaging aggregations, and investigated two MADM issues. Zhou et al. [21] devised a novel divergence measure depending on belief function, called pythagorean fuzzy set divergence measure distance, and utilized the newly developed algorithm to medical diagnostics to achieve the intended effect. Asif et al. [22] explored the use of Hamacher aggregation operators in the context of PFSs to address ambiguities commonly encountered in real-life decision-making problems. For multiattribute group decision-making (MAGDM) problems, Haq et al. [23] presented a Fermatean fuzzy Aczel-Alsina weighted average closeness coefficient aggregation operator employing the closeness coefficient. Senapati and Yager [24] explored a variety of aggregation operators and their elegant features for FFSs, whereas [25] employed a weighted product model to address multicriteria decision-making (MCDM) challenges. To deal with the multicriteria sustainable recycling partner selection circumstance in the q-ROFSs environment, Mishra and Rani [26] developed an approach that combined the additive ratio evaluation technique with concepts from the q-ROFS along with data measures as well as other implementation. Ibrahim [27] created a method for classifying children with learning disabilities in addition to a novel relation extension on n,m-ROFSs.

Circular intuitionistic fuzzy set (CIFS) is a more recent type of fuzzy set extension that Atanassov [28] explored. Each element in this set is represented as a 3-tuple that includes the membership degree, nonmembership degree, and radius. The existence of a circular imprecision area with radius ρ accounts for the distinction with IFS. The fundamental relations and operations for CIFS were first described by Atanassov [28] with ρ [ 0 , 1 ] . Later, this was enlarged to ρ [ 0 , 2 ] to encompass the entire region in the intuitionistic fuzzy interpretation triangle (IFIT) [29]. Since then, a number of studies have been conducted with the goal of expanding CIFS’s theoretical and practical applicability. Numerous studies have been carried out on CIFS; for instance, Atanassov and Marinov [29] introduced four distances for CIFS. In the framework of CIFS in group decision-making problems, Kahraman and Alkan [30] presented one of the interesting developments. In particular, they suggested creating CIF-TOPSIS, or the TOPSIS model with psychological behaviors integrated into the CIFS environment. To better distinguish between incomplete, ambiguous, and inconsistent information and to define trade-off assessments and compromise decision rules, Chen [31] developed and used CIF Minkowski distance metrics to the CIF TOPSIS methodology development process. Due to its unique properties, CIFSs can be effectively applied in multiple criteria decision analysis (MCDA) domains, helping to improve the accuracy of current MCDA models by manipulating complexly ambiguous data. The CIFS VlseKriterijumska Optimizacija I Kompromisno Resenje method was created by Kahraman and Otay [32], and it is used to solve a waste disposal chosen location issue. Boltürk and Kahraman [33] introduced novel techniques for current worth analysis that rely on interval-valued intuitionistic fuzzy sets and CIFSs. Khan et al. [34] established and examined the notion of circular PFS, an extension of CIFS, and have also examined the characteristics of the circular Pythagorean fuzzy weighted average/geometric aggregation operators. Recently, Yusoff et al. [35] extended CPFS to circular q-ROFSs (Cq-ROFSs). Following this, Ali and Yang [36] introduced an advanced version of Cq-ROFSs within Dombi aggregation operators, incorporating additional mathematical properties and algebraic laws.

CIFS, CPFS, and Cq-ROFS enhance the modeling of imprecise membership and nonmembership degrees, but each has specific limitations. CIFS is only suitable for the IFIT, where the sum of membership and nonmembership degrees is constrained to one. Likewise, CPFS is not applicable when the square sum of membership and nonmembership degrees exceeds one. Similarly, Cq-ROFS is not applicable in cases where the q sum of the membership and nonmembership degrees surpasses one. This restriction can be removed by adding the bigger space offered by n,m-ROFS to the current IFIT area in CIFS. In light of the aforementioned constraints and knowledge gaps, the key objectives of this paper’s research contribution are as follows. To create a circular n,m-rung orthopair fuzzy set (Cn,m-ROFS) that not just can simulate inaccurate membership and nonmembership degrees but additionally covers a larger area of imprecision, allowing the total of membership and nonmembership degrees to be greater than one. We start by systematically describing the set form of Cn,m-ROFS, as well as the corresponding relations and operations. The material that follows focuses on algebraic operations such as intersection, union, algebraic sum, and algebraic product. We also look at features like idempotency, inclusion, and absorption to see how these operations behave in Cn,m-ROFS. In addition, we construct and explore various modal aggregation operators to add to the theory of Cn,m-ROFS. Furthermore, we provide an approach to MCDM utilizing these aggregation operators in a Cn,m-ROF setting.

The current study’s motivation is characterized as follows: while dealing with two-dimensional uncertainty, n,m-ROFSs have a broader range of applications than IFSs, PFSs, FFSs, and q-ROFSs. As a result, the concepts of CIFS and CPFS are extended to Cn,m-ROFS in this study work, making it easier to deal with new uncertainties.

The following are some of the primary contributions of this study.

  1. A fresh and strong extension of the CIFS approach called Cn,m-ROFS, is presented, allowing efficient handling of n,m-ROFS material. A Cn,m-ROFS has circles to indicate which elements are members and nonmembers. Due to its unique structure, this approach allows for a more sensitive modeling of continuous environments in MCDM theory. For example, consider a decision-maker providing the circular 2,4-rung orthopair fuzzy value ( 0.89 , 0.67 ; 0.05 ) . This outcome cannot be captured by CIFS, CPFS, or Cq-ROFS models because, in these models, the sum 0.8 9 q + 0.6 7 q exceeds 1 for q 3 .

  2. Several fundamental operations of the recently constructed model are given and illustrated with examples, such as subset, complement, intersection, union, scalar multiplication, multiplication, and addition.

  3. One more efficient approach that works in a Cn,m-ROF environment is used to address real-life MCDM problems, such as choosing the best teacher and school.

  4. In addition, included is a comparison between some of the current techniques and the proposed MCDM technique based on Cn,m-ROFSs.

This article is set up in the following way: Section 2 contains some preliminary information, such as IFS, PFS, q-ROFS, n,m-ROFS, CIFS, and CPFS. Section 3 presents Cn,m-ROFSs and examines several of its set-theoretic operations. Section 4 investigates the use of the circular weighted average and geometric aggregation operations on Cn,m-ROFS data. Section 5 provides a method for MCDM focused on the Cn,m-ROFS environment, and real-life instances are included to demonstrate the utility of the proposed decision-making process. Section 6 conducts a comparison analysis to show that the proposed method betters other MCDM techniques. Section 7 focuses on the sensitivity analysis and the limitations of the proposed aggregation operators. Section 8 offers an outcome as well as a strategy for future development.

2 Preliminaries

In this section, we covered some fundamental concepts that would be useful for the proposed work, such as IFS, PFS, q-ROFS, n,m-ROFS, CIFS, and CPFS.

Definition 2.1

Assume that Ψ is a universe set. Let μ ¯ Ω ^ : Ψ [ 0 , 1 ] be the degree of membership and μ ̲ Ω ^ : Ψ [ 0 , 1 ] be the degree of nonmembership of ζ Ψ to Ω ^ . Then, Ω ^ = { ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) : ζ Ψ } is called

  1. an IFS [2] if μ ¯ Ω ^ + μ ̲ Ω ^ 1 ;

  2. a PFS [3] if ( μ ¯ Ω ^ ) 2 + ( μ ̲ Ω ^ ) 2 1 ;

  3. a q-ROFS [4] if ( μ ¯ Ω ^ ) q + ( μ ̲ Ω ^ ) q 1 , for q > 2 ;

  4. an n,m-ROFS [6] if ( μ ¯ Ω ^ ) n + ( μ ̲ Ω ^ ) m 1 , for n , m > 1 .

Definition 2.2

[28] A CIFS Ω ^ of radius ρ ^ over a universal set Ψ is recognized as follows:

Ω ^ = { ( ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) ζ Ψ } ,

when both membership μ ¯ Ω ^ and nonmembership μ ̲ Ω ^ meet the requirement μ ¯ Ω ^ ( ζ ) + μ ̲ Ω ^ ( ζ ) 1 for all ζ Ψ , and ρ ^ [ 0 , 1 ] represents the radius of a circle with the center ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ) . The term π Ω ^ ( ζ ) = 1 ( μ ¯ Ω ^ ( ζ ) + μ ̲ Ω ^ ( ζ ) ) expresses the degree of hesitation of an element ζ Ψ .

Definition 2.3

[34] A CPFS Ω ^ of radius ρ ^ over a universal set Ψ is recognized as follows:

Ω ^ = { ( ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) ζ Ψ } ,

when both membership μ ¯ Ω ^ and nonmembership μ ̲ Ω ^ meet the requirement μ ¯ Ω ^ 2 ( ζ ) + μ ̲ Ω ^ 2 ( ζ ) 1 for all ζ Ψ , and ρ ^ [ 0 , 2 ] represents the radius of a circle with the center ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ) . The term π Ω ^ ( ζ ) = 1 ( μ ¯ Ω ^ 2 ( ζ ) + μ ̲ Ω ^ 2 ( ζ ) ) expresses the degree of hesitation of an element ζ Ψ .

The primary abbreviations used throughout this article are presented in Table 1.

Table 1

Abbreviations of the main concepts presented in this manuscript

Concepts Abbreviation
Fuzzy set FS
Intuitionistic fuzzy set IFS
Pythagorean fuzzy set PFS
Fermatean fuzzy set FFS
q-rung orthopair fuzzy set q-ROFS
n,m-rung orthopair fuzzy set n,m-ROFS
Intuitionistic fuzzy interpretation triangle IFIT
Circular intuitionistic fuzzy set CIFS
Circular Pythagorean fuzzy set CPFS
Circular q-rung orthopair fuzzy set Cq-ROFS
Circular n,m-rung orthopair fuzzy set Cn,m-ROFS
Circular n,m-rung orthopair fuzzy value Cn,m-ROFV
Intuitionistic fuzzy weighted average IFWA
Intuitionistic fuzzy weighted geometric IFWG
q-rung orthopair fuzzy weighted averaging q-ROFWA
q-rung orthopair fuzzy weighted geometric q-ROFWG
Circular Pythagorean fuzzy weighted average max CPFWAmax
Circular Pythagorean fuzzy weighted average min CPFWAmin
Circular n,m-rung orthopair fuzzy weighted average max Cn,m-ROFWAmax
Circular n,m-rung orthopair fuzzy weighted average min Cn,m-ROFWAmin
Circular n,m-rung orthopair fuzzy weighted geometric max Cn,m-ROFWGmax
Circular n,m-rung orthopair fuzzy weighted geometric min Cn,m-ROFWGmin
Decision matrix DM
Multicriteria decision-making MCDM
Multiattribute decision-making MADM
Multiattribute group decision-making MAGDM
Multiple criteria decision analysis MCDA

3 Circular n,m-ROFS

The basic concepts and operations of Cn,m-ROFS are described in this section.

Definition 3.1

A circular n,m-rung orthopair fuzzy set (Cn,m-ROFS) Ω ^ of radius ρ ^ over a universal set Ψ is recognized as follows:

Ω ^ = { ( ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) ζ Ψ } ,

when both membership μ ¯ Ω ^ and nonmembership μ ̲ Ω ^ meet the requirement μ ¯ Ω ^ n ( ζ ) + μ ̲ Ω ^ m ( ζ ) 1 for all ζ Ψ , and ρ ^ [ 0 , 2 ] represents the radius of a circle with the center ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ) . The term π Ω ^ ( ζ ) = 1 ( μ ¯ Ω ^ n ( ζ ) + μ ̲ Ω ^ m ( ζ ) ) n + m expresses the degree of hesitation of an element ζ Ψ .

The calculation for ζ , Ω ^ = ( ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) represents the circular n,m-rung orthopair fuzzy value (Cn,m-ROFV), which is a circle with radius ρ ^ at center ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ) . A Cn,m-ROFV expressed in an abstract environment is written as ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) instead of the ( ζ , μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) expression linked to a different ζ . When ρ ^ is equal to zero, the Cn,m-ROFS functions transform into a regular n,m-ROFS.

The concepts of Cn,m-ROFSs are what we suggest to obtain beyond the constrictive realm of CIFSs and CPFSs. With geometrical visualizations provided in Figure 1, this is a valid extension of all IFSs, PFSs, q-ROFSs, n,m-ROFSs, CIFSs, and CPFSs. Look at Figure 1.

  1. The CIFS modelization permits circle 1, but not circles 2, 3, 4, and 5.

  2. The CPFS modelization permits circles 1 and 2, but not circles 3, 4, and 5.

  3. The C2,3-ROFS modelization permits circles 1, 2 and 3, but not circles 4 and 5.

  4. The C3,2-ROFS modelization permits circles 1, 2 and 4, but not circles 3 and 5.

  5. The C4,5-ROFS modelization permits circles 1, 2, 3, 4 and 5.

Figure 1 
               CIFSs and CPFSs fundamental components are compared geometrically with Cn,m-ROFS.
Figure 1

CIFSs and CPFSs fundamental components are compared geometrically with Cn,m-ROFS.

Definition 3.2

Let Ω ^ = { ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) ζ Ψ } , Ω ^ 1 = { ( μ ¯ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 1 ( ζ ) ; ρ ^ 1 ) ζ Ψ } and Ω ^ 2 = { ( μ ¯ Ω ^ 2 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) ; ρ ^ 2 ) ζ Ψ } be three Cn,m-ROFSs. Then, the next operations are recognized:

  1. Ω ^ c = μ ̲ Ω ^ ( ζ ) m n , μ ¯ Ω ^ ( ζ ) n m ; ρ ^ ζ Ψ .

  2. Ω ^ 1 Ω ^ 2 if and only if ρ ^ 1 ρ ^ 2 , μ ¯ Ω ^ 1 ( ζ ) μ ¯ Ω ^ 2 ( ζ ) and μ ̲ Ω ^ 1 ( ζ ) μ ̲ Ω ^ 2 ( ζ ) .

  3. Ω ^ 1 Ω ^ 2 if Ω ^ 1 Ω ^ 2 .

  4. Ω ^ 1 ̲ max Ω ^ 2 = { ( max { μ ¯ Ω ^ 1 ( ζ ) , μ ¯ Ω ^ 2 ( ζ ) } , min { μ ̲ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) } ; max { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } ,

    and

    Ω ^ 1 ̲ min Ω ^ 2 = { ( max { μ ¯ Ω ^ 1 ( ζ ) , μ ¯ Ω ^ 2 ( ζ ) } , min { μ ̲ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) } ; min { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } .

  5. Ω ^ 1 ¯ max Ω ^ 2 = { ( min { μ ¯ Ω ^ 1 ( ζ ) , μ ¯ Ω ^ 2 ( ζ ) } , max { μ ̲ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) } ; max { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } ,

    and

    Ω ^ 1 ¯ min Ω ^ 2 = { ( min { μ ¯ Ω ^ 1 ( ζ ) , μ ¯ Ω ^ 2 ( ζ ) } , max { μ ̲ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) } ; min { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } .

Example 3.3

Let Ψ = { ζ 1 , ζ 2 } , then

Ω ^ 1 = ( ζ 1 , 0.56 , 0.88 ; 0.02 ) ( ζ 2 , 0.67 , 0.49 ; 0.03 )

and

Ω ^ 2 = ( ζ 1 , 0.75 , 0.63 ; 0.05 ) ( ζ 2 , 0.91 , 0.55 ; 0.01 )

be two C2,3-ROFSs in Ψ . Then,

Ω ^ 1 c = ζ 1 , ( 0.88 ) 3 2 , ( 0.56 ) 2 3 ; 0.02 ζ 2 , ( 0.49 ) 3 2 , ( 0.67 ) 2 3 ; 0.03 , Ω ^ 2 c = ζ 1 , ( 0.63 ) 3 2 , ( 0.75 ) 2 3 ; 0.05 ζ 2 , ( 0.55 ) 3 2 , ( 0.91 ) 2 3 ; 0.01 , Ω ^ 1 ̲ max Ω ^ 2 = ( ζ 1 , 0.75 , 0.63 ; 0.05 ) ( ζ 2 , 0.91 , 0.49 ; 0.03 ) , Ω ^ 1 ̲ min Ω ^ 2 = ( ζ 1 , 0.75 , 0.63 ; 0.02 ) ( ζ 2 , 0.91 , 0.49 ; 0.01 ) , Ω ^ 1 ¯ max Ω ^ 2 = ( ζ 1 , 0.56 , 0.88 ; 0.05 ) ( ζ 2 , 0.67 , 0.55 ; 0.03 ) ,

and

Ω ^ 1 ¯ min Ω ^ 2 = ( ζ 1 , 0.56 , 0.88 ; 0.02 ) ( ζ 2 , 0.67 , 0.55 ; 0.01 ) .

Definition 3.4

Let Ω ^ = { ( μ ¯ Ω ^ ( ζ ) , μ ̲ Ω ^ ( ζ ) ; ρ ^ ) ζ Ψ } , Ω ^ 1 = { ( μ ¯ Ω ^ 1 ( ζ ) , μ ̲ Ω ^ 1 ( ζ ) ; ρ ^ 1 ) ζ Ψ } , and Ω ^ 2 = { ( μ ¯ Ω ^ 2 ( ζ ) , μ ̲ Ω ^ 2 ( ζ ) ; ρ ^ 2 ) ζ Ψ } be three Cn,m-ROFSs. Then, the next operations are recognized:

  1. Ω ^ 1 max Ω ^ 2 = { ( μ ¯ Ω ^ 1 n ( ζ ) + μ ¯ Ω ^ 2 n ( ζ ) μ ¯ Ω ^ 1 n ( ζ ) μ ¯ Ω ^ 2 n ( ζ ) n , μ ̲ Ω ^ 1 ( ζ ) μ ̲ Ω ^ 2 ( ζ ) ; max { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } ,

    and

    Ω ^ 1 min Ω ^ 2 = { ( μ ¯ Ω ^ 1 n ( ζ ) + μ ¯ Ω ^ 2 n ( ζ ) μ ¯ Ω ^ 1 n ( ζ ) μ ¯ Ω ^ 2 n ( ζ ) n , μ ̲ Ω ^ 1 ( ζ ) μ ̲ Ω ^ 2 ( ζ ) ; min { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } .

  2. Ω ^ 1 max Ω ^ 2 = { ( μ ¯ Ω ^ 1 ( ζ ) μ ¯ Ω ^ 2 ( ζ ) , μ ̲ Ω ^ 1 m ( ζ ) + μ ̲ Ω ^ 2 m ( ζ ) μ ̲ Ω ^ 1 m ( ζ ) μ ̲ Ω ^ 2 m ( ζ ) m ; max { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } ,

    and

    Ω ^ 1 min Ω ^ 2 = { ( μ ¯ Ω ^ 1 ( ζ ) μ ¯ Ω ^ 2 ( ζ ) , μ ̲ Ω ^ 1 m ( ζ ) + μ ̲ Ω ^ 2 m ( ζ ) μ ̲ Ω ^ 1 m ( ζ ) μ ̲ Ω ^ 2 m ( ζ ) ; m min { ρ ^ 1 , ρ ^ 2 } ) ζ Ψ } .

  3. ε Ω ^ = { ( 1 ( 1 μ ¯ Ω ^ n ( ζ ) ) ε n , μ ̲ Ω ^ ε ( ζ ) ; ρ ^ ) ζ Ψ } ,

    and

    Ω ^ ε = { ( μ ¯ Ω ^ ε ( ζ ) , 1 ( 1 μ ̲ Ω ^ m ( ζ ) ) ε m ; ρ ^ ) ζ Ψ } ,

    for ε > 0 .

Example 3.5

Assume that both Ω ^ 1 = ( 0.89 , 0.61 ; 0.02 ) and Ω ^ 2 = ( 0.74 , 0.83 ; 0.04 ) are C4,2-ROFVs for Ψ = { ζ } . Then,

  1. Ω ^ 1 max Ω ^ 2 = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 0.8 9 4 + 0.7 4 4 ( 0.89 ) 4 ( 0.74 ) 4 4 , ( 0.61 ) ( 0.83 ) ; max { 0.02 , 0.04 } ) ( 0.9272 , 0.5063 ; 0.04 ) .

  2. Ω ^ 1 min Ω ^ 2 = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; min { ρ ^ 1 , ρ ^ 2 } ) = ( 0.8 9 4 + 0.7 4 4 ( 0.89 ) 4 ( 0.74 ) 4 4 , ( 0.61 ) ( 0.83 ) ; min { 0.02 , 0.04 } ) ( 0.9272 , 0.5063 ; 0.02 ) .

  3. Ω ^ 1 max Ω ^ 2 = ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 , μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m ; max { ρ ^ 1 , ρ ^ 2 } ) = ( ( 0.89 ) ( 0.74 ) , 0.6 1 2 + 0.8 3 2 ( 0.61 ) 2 ( 0.83 ) 2 ; max { 0.02 , 0.04 } ) ( 0.6586 , 0.8970 ; 0.04 ) .

  4. Ω ^ 1 min Ω ^ 2 = ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 , μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m ; min { ρ ^ 1 , ρ ^ 2 } ) = ( ( 0.89 ) ( 0.74 ) , 0.6 1 2 + 0.8 3 2 ( 0.61 ) 2 ( 0.83 ) 2 ; min { 0.02 , 0.04 } ) ( 0.6586 , 0.8970 ; 0.02 ) .

  5. ε Ω ^ 1 = ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε n , μ ̲ Ω ^ 1 ε ; ρ ^ ) = ( 1 ( 1 0.8 9 4 ) 5 4 , 0.6 1 5 ; 0.02 ) ( 0.9982 , 0.0845 ; 0.02 ) , for ε = 5 .

  6. Ω ^ 1 ε = ( μ ¯ Ω ^ 1 ε , 1 ( 1 μ ̲ Ω ^ 1 m ) ε m ; ρ ^ ) = ( 0.8 9 5 , 1 ( 1 0.6 1 2 ) 5 ; 0.02 ) ( 0.5584 , 0.9499 ; 0.02 ) , for ε = 5 .

Theorem 3.6

If Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) is a Cn,m-ROFV, then Ω ^ c is also a Cn,m-ROFV and ( Ω ^ c ) c = Ω ^ .

Proof

Since 0 μ ¯ Ω ^ n 1 , 0 μ ̲ Ω ^ m 1 and 0 μ ¯ Ω ^ n + μ ̲ Ω ^ m 1 , then

0 ( μ ̲ Ω ^ m n ) n + ( μ ¯ Ω ^ n m ) m = μ ̲ Ω ^ m + μ ¯ Ω ^ n 1

and hence,

0 ( μ ̲ Ω ^ m n ) n + ( μ ¯ Ω ^ n m ) m 1 .

Thus, Ω ^ c is a Cn,m-ROFS, and it is clear that ( Ω ^ c ) c = ( μ ̲ Ω ^ m n , μ ¯ Ω ^ n m ; ρ ^ ) c = ( ( μ ¯ Ω ^ n m ) m n , ( μ ̲ Ω ^ m n ) n m ; ρ ^ ) = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) .□

Theorem 3.7

If Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) are two Cn,m-ROFVs, then Ω ^ 1 max Ω ^ 2 , Ω ^ 1 min Ω ^ 2 , Ω ^ 1 max Ω ^ 2 , and Ω ^ 1 min Ω ^ 2 are also Cn,m-ROFVs.

Proof

For Cn,m-ROFSs Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) it is clear that the following relationships exist,

0 μ ¯ Ω ^ 1 n 1 , 0 μ ̲ Ω ^ 1 m 1 , 0 μ ¯ Ω ^ 1 n + μ ̲ Ω ^ 1 m 1 ,

and

0 μ ¯ Ω ^ 2 n 1 , 0 μ ̲ Ω ^ 2 m 1 , 0 μ ¯ Ω ^ 2 n + μ ̲ Ω ^ 2 m 1 .

Then, we have

μ ¯ Ω ^ 1 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n , μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n , 1 μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n 0 ,

and

μ ̲ Ω ^ 1 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m , μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m , 1 μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m 0 ,

which demonstrates that

μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n 0 implies μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n 0 ,

and

μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m 0 implies μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m 0 .

Since μ ¯ Ω ^ 2 n 1 and 0 1 μ ¯ Ω ^ 1 n , then μ ¯ Ω ^ 2 n ( 1 μ ¯ Ω ^ 1 n ) ( 1 μ ¯ Ω ^ 1 n ) , and we obtain μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n 1 , and hence, μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n 1 .

In the same way, we may obtain

μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m 1 .

It is clear that

0 μ ̲ Ω ^ 1 m 1 μ ¯ Ω ^ 1 n and 0 μ ̲ Ω ^ 2 m 1 μ ¯ Ω ^ 2 n ,

thereafter, we can obtain

( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n ) n + ( μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ) m μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n + ( 1 μ ¯ Ω ^ 1 n ) ( 1 μ ¯ Ω ^ 2 n ) = 1 .

Thus,

0 μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n 1 , 0 μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 1

and

0 ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n ) n + ( μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ) m 1 .

In a similar manner, we have

0 μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 1 , 0 μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m 1

and

0 ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 ) n + ( μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m ) m 1 .

These demonstrates that Ω ^ 1 max Ω ^ 2 , Ω ^ 1 min Ω ^ 2 , Ω ^ 1 max Ω ^ 2 , and Ω ^ 1 min Ω ^ 2 are Cn,m-ROFSs.□

Theorem 3.8

Let Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) be a Cn,m-ROFV and ε > 0 . Then, ε Ω ^ and Ω ^ ε are also Cn,m-ROFVs.

Proof

Since 0 μ ¯ Ω ^ n 1 , 0 μ ̲ Ω ^ m 1 and 0 μ ¯ Ω ^ n + μ ̲ Ω ^ m 1 , then

0 μ ̲ Ω ^ m 1 μ ¯ Ω ^ n 0 ( 1 μ ¯ Ω ^ n ) ε 1 ( 1 μ ¯ Ω ^ n ) ε 1 0 1 ( 1 μ ¯ Ω ^ n ) ε n 1 n = 1 .

It is clear that 0 μ ̲ Ω ^ ε 1 , thereafter, we can obtain

0 ( 1 ( 1 μ ¯ Ω ^ n ) ε n ) n + ( μ ̲ Ω ^ ε ) m 1 ( 1 μ ¯ Ω ^ n ) ε + ( 1 μ ¯ Ω ^ n ) ε = 1 .

In the same way, we can also acquire

0 ( μ ¯ Ω ^ ε ) n + ( 1 ( 1 μ ̲ Ω ^ m ) ε m ) m 1 .

Therefore, ε Ω ^ and Ω ^ ε are Cn,m-ROFSs.□

Theorem 3.9

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) be two Cn,m-ROFVs. Then,

  1. Ω ^ 1 ̲ max Ω ^ 2 = Ω ^ 2 ̲ max Ω ^ 1 .

  2. Ω ^ 1 ̲ min Ω ^ 2 = Ω ^ 2 ̲ min Ω ^ 1 .

  3. Ω ^ 1 ¯ max Ω ^ 2 = Ω ^ 2 ¯ max Ω ^ 1 .

  4. Ω ^ 1 ¯ min Ω ^ 2 = Ω ^ 2 ¯ min Ω ^ 1 .

  5. Ω ^ 1 max Ω ^ 2 = Ω ^ 2 max Ω ^ 1 .

  6. Ω ^ 1 min Ω ^ 2 = Ω ^ 2 min Ω ^ 1 .

  7. Ω ^ 1 max Ω ^ 2 = Ω ^ 2 max Ω ^ 1 .

  8. Ω ^ 1 min Ω ^ 2 = Ω ^ 2 min Ω ^ 1 .

Proof

Parts 1 and 5 will be demonstrated here. In a similar manner, the other parts can be shown.

  1. Ω ^ 1 ̲ max Ω ^ 2 = ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( max { μ ¯ Ω ^ 2 , μ ¯ Ω ^ 1 } , min { μ ̲ Ω ^ 2 , μ ̲ Ω ^ 1 } ; max { ρ ^ 2 , ρ ^ 1 } ) = Ω ^ 2 ̲ max Ω ^ 1 .

  2. Ω ^ 1 max Ω ^ 2 = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ¯ Ω ^ 2 n + μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n n , μ ̲ Ω ^ 2 μ ̲ Ω ^ 1 ; max { ρ ^ 2 , ρ ^ 1 } ) = Ω ^ 2 Ω ^ 1 .

Theorem 3.10

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) be two Cn,m-ROFVs, and ε > 0 . Then,

  1. ε ( Ω ^ 1 ̲ max Ω ^ 2 ) = ε Ω ^ 1 ̲ max ε Ω ^ 2 .

  2. ε ( Ω ^ 1 ̲ min Ω ^ 2 ) = ε Ω ^ 1 ̲ min ε Ω ^ 2 .

  3. ( Ω ^ 1 ̲ max Ω ^ 2 ) ε = Ω ^ 1 ε ̲ max Ω ^ 2 ε .

  4. ( Ω ^ 1 ̲ min Ω ^ 2 ) ε = Ω ^ 1 ε ̲ min Ω ^ 2 ε .

  5. ε ( Ω ^ 1 max Ω ^ 2 ) = ε Ω ^ 1 max ε Ω ^ 2 .

  6. ε ( Ω ^ 1 min Ω ^ 2 ) = ε Ω ^ 1 min ε Ω ^ 2 .

  7. ( Ω ^ 1 max Ω ^ 2 ) ε = Ω ^ 1 ε max Ω ^ 2 ε .

  8. ( Ω ^ 1 min Ω ^ 2 ) ε = Ω ^ 1 ε min Ω ^ 2 ε .

Proof

Parts 1, 5, and 7 will be demonstrated here. In a similar manner, the other parts can be shown.

  1. ε ( Ω ^ 1 ̲ max Ω ^ 2 ) = ε ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 1 ( 1 max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } ) ε n , min { μ ̲ Ω ^ 1 ε , μ ̲ Ω ^ 2 ε } ; max { ρ ^ 1 , ρ ^ 2 } ) ,

    and

    ε Ω ^ 1 ̲ max ε Ω ^ 2 = ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε n , μ ̲ Ω ^ 1 ε ; ρ ^ 1 ) ̲ max ( 1 ( 1 μ ¯ Ω ^ 2 n ) ε n , μ ̲ Ω ^ 2 ε ; ρ ^ 2 ) = ( max { 1 ( 1 μ ¯ Ω ^ 1 n ) ε n , 1 ( 1 μ ¯ Ω ^ 2 n ) ε n } , min { μ ̲ Ω ^ 1 ε , μ ̲ Ω ^ 2 ε } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 1 ( 1 max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } ) ε n , min { μ ̲ Ω ^ 1 ε , μ ̲ Ω ^ 2 ε } ; max { ρ ^ 1 , ρ ^ 2 } ) = ε ( Ω ^ 1 ̲ max Ω ^ 2 ) .

  2. ε ( Ω ^ 1 max Ω ^ 2 ) = ε ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 1 ( 1 μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n + μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n ) n ε , ( μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ) ε ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε ( 1 μ ¯ Ω ^ 2 n ) ε n , μ ̲ Ω ^ 1 ε μ ̲ Ω ^ 2 ε ; max { ρ ^ 1 , ρ ^ 2 } )

    and

    ε Ω ^ 1 max ε Ω ^ 2 = ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε n , μ ̲ Ω ^ 1 ε ; ρ ^ 1 ) max ( 1 ( 1 μ ¯ Ω ^ 2 n ) ε n , μ ̲ Ω ^ 2 ε ; ρ ^ 2 ) = ( ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε + 1 ( 1 μ ¯ Ω ^ 2 n ) ε ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε ) ( 1 ( 1 μ ¯ Ω ^ 2 n ) ε ) ) 1 n , μ ̲ Ω ^ 1 ε μ ̲ Ω ^ 2 ε ; max { ρ ^ 1 , ρ ^ 2 } ) = ( 1 ( 1 μ ¯ Ω ^ 1 n ) ε ( 1 μ ¯ Ω ^ 2 n ) ε n , μ ̲ Ω ^ 1 ε μ ̲ Ω ^ 2 ε ; max { ρ ^ 1 , ρ ^ 2 } ) = ε ( Ω ^ 1 Ω ^ 2 ) .

  3. ( Ω ^ 1 max Ω ^ 2 ) ε = ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 , μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m ; max { ρ ^ 1 , ρ ^ 2 } ) ε = ( ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 ) ε , 1 ( 1 μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m + μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m ) ε m ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ¯ Ω ^ 1 ε μ ¯ Ω ^ 2 ε , 1 ( 1 μ ̲ Ω ^ 1 m ) ε ( 1 μ ̲ Ω ^ 2 m ) ε m ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ¯ Ω ^ 1 ε , 1 ( 1 μ ̲ Ω ^ 1 m ) ε m ; ρ ^ 1 ) ( μ ¯ Ω ^ 2 ε , 1 ( 1 μ ̲ Ω ^ 2 m ) ε m ; ρ ^ 2 ) = Ω ^ 1 ε max Ω ^ 2 ε .

Theorem 3.11

Let Ω ^ = ( μ ¯ , μ ̲ ; ρ ^ ) be a Cn,m-ROFV, and ε 1 , ε 2 > 0 . Then,

  1. ( ε 1 + ε 2 ) Ω ^ = ε 1 Ω ^ max ε 2 Ω ^ .

  2. ( ε 1 + ε 2 ) Ω ^ = ε 1 Ω ^ min ε 2 Ω ^ .

  3. Ω ^ ε 1 + ε 2 = Ω ^ ε 1 max Ω ^ ε 2 .

  4. Ω ^ ε 1 + ε 2 = Ω ^ ε 1 min Ω ^ ε 2 .

Proof

Parts 1 and 3 will be demonstrated here. In a similar manner, the other parts can be shown.

  1. ( ε 1 + ε 2 ) Ω ^ = ( ε 1 + ε 2 ) ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) = ( 1 ( 1 μ ¯ Ω ^ n ) ε 1 + ε 2 n , μ ̲ Ω ^ ε 1 + ε 2 ; ρ ^ ) = ( 1 ( 1 μ ¯ Ω ^ n ) ε 1 ( 1 μ ¯ Ω ^ n ) ε 2 n , μ ̲ Ω ^ ε 1 + ε 2 ; ρ ^ ) = ( ( 1 ( 1 μ ¯ Ω ^ n ) ε 1 + 1 ( 1 μ ¯ Ω ^ n ) ε 2 ( 1 ( 1 μ ¯ Ω ^ n ) ε 1 ) ( 1 ( 1 μ ¯ Ω ^ n ) ε 2 ) ) 1 n , μ ̲ Ω ^ ε 1 μ ̲ Ω ^ ε 2 ; max { ρ ^ , ρ ^ } ) = ( 1 ( 1 μ ¯ Ω ^ n ) ε 1 n , μ ̲ Ω ^ ε 1 ; ρ ^ ) max ( 1 ( 1 μ ¯ Ω ^ n ) ε 2 n , μ ̲ Ω ^ ε 2 ; ρ ^ ) = ε 1 Ω ^ max ε 2 Ω ^ .

  2. Ω ^ ( ε 1 + ε 2 ) = ( μ ¯ Ω ^ ε 1 + ε 2 , 1 ( 1 μ ̲ Ω ^ m ) ε 1 + ε 2 m ; ρ ^ ) = ( μ ¯ Ω ^ ε 1 + ε 2 , 1 ( 1 μ ̲ Ω ^ m ) ε 1 + ε 2 m ; max { ρ ^ , ρ ^ } ) = ( μ ¯ Ω ^ ε 1 , 1 ( 1 μ ̲ Ω ^ m ) ε 1 m ; ρ ^ ) max ( μ ¯ Ω ^ ε 2 , 1 ( 1 μ ̲ Ω ^ m ) ε 2 m ; ρ ^ ) = Ω ^ ε 1 max Ω ^ ε 2 .

Theorem 3.12

Let Ω ^ = ( μ ¯ , μ ̲ ; ρ ^ ) be a Cn,m-ROFVs, and ε > 0 . Then,

  1. ε ( Ω ^ c ) = ( Ω ^ ε ) c .

  2. ( Ω ^ c ) ε = ( ε Ω ^ ) c .

Proof

  1. ε ( Ω ^ ) c = ε ( μ ̲ Ω ^ m n , μ ¯ Ω ^ n m ; ρ ^ ) = ( 1 ( 1 μ ̲ Ω ^ m ) ε n , ( μ ¯ Ω ^ n m ) ε ; ρ ^ ) = ( μ ¯ Ω ^ ε , 1 ( 1 μ ̲ Ω ^ m ) ε m ; ρ ^ ) c = ( Ω ^ ε ) c .

  2. ( Ω ^ c ) ε = ( μ ̲ Ω ^ m n , μ ¯ Ω ^ n m ; ρ ^ ) ε = ( ( μ ̲ Ω ^ m n ) ε , 1 ( 1 μ ¯ Ω ^ n ) ε m ; ρ ^ ) , and ( ε Ω ^ ) c = ( 1 ( 1 μ ¯ n ) ε n , μ ̲ ε ; ρ ^ ) c ( ( μ ̲ ε ) m n , 1 ( 1 μ ¯ n ) ε m ; ρ ^ ) = ( Ω ^ c ) ε .

Theorem 3.13

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) be two Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ¯ max Ω ^ 2 ) c = Ω ^ 1 c ̲ max Ω ^ 2 c .

  2. ( Ω ^ 1 ¯ min Ω ^ 2 ) c = Ω ^ 1 c ̲ min Ω ^ 2 c .

  3. ( Ω ^ 1 ̲ max Ω ^ 2 ) c = Ω ^ 1 c ¯ max Ω ^ 2 c .

  4. ( Ω ^ 1 ̲ min Ω ^ 2 ) c = Ω ^ 1 c ¯ min Ω ^ 2 c .

  5. ( Ω ^ 1 max Ω ^ 2 ) c = Ω ^ 1 c max Ω ^ 2 c .

  6. ( Ω ^ 1 min Ω ^ 2 ) c = Ω ^ 1 c min Ω ^ 2 c .

  7. ( Ω ^ 1 max Ω ^ 2 ) c = Ω ^ 1 c max Ω ^ 2 c .

  8. ( Ω ^ 1 min Ω ^ 2 ) c = Ω ^ 1 c min Ω ^ 2 c .

Proof

Parts 1 and 5 will be demonstrated here. In a similar manner, the other parts can be shown.

  1. ( Ω ^ 1 ¯ max Ω ^ 2 ) c = ( min { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , max { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) c = ( max { μ ̲ Ω ^ 1 m n , μ ̲ Ω ^ 2 m n } , min { μ ¯ Ω ^ 1 n m , μ ¯ Ω ^ 2 n m } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ̲ Ω ^ 1 m n , μ ¯ Ω ^ 1 n m ; ρ ^ 1 ) ̲ max ( μ ̲ Ω ^ 2 m n , μ ¯ Ω ^ 2 n m ; ρ ^ 2 ) = Ω ^ 1 c ̲ max Ω ^ 2 c .

  2. ( Ω ^ 1 max Ω ^ 2 ) c = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) c = ( ( μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ) m n , μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n m ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ̲ Ω ^ 1 m n μ ̲ Ω ^ 2 m n , μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n m ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ̲ Ω ^ 1 m n , μ ¯ Ω ^ 1 n m ; ρ ^ 1 ) max ( μ ̲ Ω ^ 2 m n , μ ¯ Ω ^ 2 n m : ρ ^ 2 ) = Ω ^ 1 c max Ω ^ 2 c .

Theorem 3.14

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) be two Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

  2. ( Ω ^ 1 ̲ min Ω ^ 2 ) min ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  3. ( Ω ^ 1 ̲ max Ω ^ 2 ) min ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

  4. ( Ω ^ 1 ̲ min Ω ^ 2 ) max ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  5. ( Ω ^ 1 ̲ max Ω ^ 2 ) min ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  6. ( Ω ^ 1 ̲ min Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

  7. ( Ω ^ 1 ̲ max Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

  8. ( Ω ^ 1 ̲ min Ω ^ 2 ) min ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  9. ( Ω ^ 1 ̲ max Ω ^ 2 ) min ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

  10. ( Ω ^ 1 ̲ min Ω ^ 2 ) max ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  11. ( Ω ^ 1 ̲ max Ω ^ 2 ) min ( Ω ^ 1 ¯ min Ω ^ 2 ) = Ω ^ 1 min Ω ^ 2 .

  12. ( Ω ^ 1 ̲ min Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = Ω ^ 1 max Ω ^ 2 .

Proof

Parts 1 and 7 will be demonstrated here. In a similar manner, the other parts can be shown.

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) max ( min { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , max { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 2 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 2 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) = Ω ^ 1 max Ω ^ 2 .

  2. ( Ω ^ 1 ̲ max Ω ^ 2 ) max ( Ω ^ 1 ¯ max Ω ^ 2 ) = ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) max ( min { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , max { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) = ( μ ¯ Ω ^ 1 μ ¯ Ω ^ 2 , μ ̲ Ω ^ 1 m + μ ̲ Ω ^ 2 m μ ̲ Ω ^ 1 m μ ̲ Ω ^ 2 m m ; max { ρ ^ 1 , ρ ^ 2 } ) = Ω ^ 1 max Ω ^ 2 .

Theorem 3.15

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) be two Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) ¯ min Ω ^ 2 = Ω ^ 2 .

  2. ( Ω ^ 1 ̲ min Ω ^ 2 ) ¯ max Ω ^ 2 = Ω ^ 2 .

  3. ( Ω ^ 1 ¯ max Ω ^ 2 ) ̲ min Ω ^ 2 = Ω ^ 2 .

  4. ( Ω ^ 1 ¯ min Ω ^ 2 ) ̲ max Ω ^ 2 = Ω ^ 2 .

Proof

The first part only has to be proven; the rest are analogous.

(1)

( Ω ^ 1 ̲ max Ω ^ 2 ) ¯ min Ω ^ 2 = ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) ¯ min ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) = ( min { max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , μ ¯ Ω ^ 2 } , max { min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } , μ ̲ Ω ^ 2 } ; min { max { ρ ^ 1 , ρ ^ 2 } , ρ ^ 2 } ) = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) = Ω ^ 2 .

Theorem 3.16

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) , Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) and Ω ^ 3 = ( μ ¯ Ω ^ 3 , μ ̲ Ω ^ 3 ; ρ ^ 3 ) be three Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) ¯ min Ω ^ 3 = ( Ω ^ 1 ¯ min Ω ^ 3 ) ̲ max ( Ω ^ 2 ¯ min Ω ^ 3 ) .

  2. ( Ω ^ 1 ̲ min Ω ^ 2 ) ¯ max Ω ^ 3 = ( Ω ^ 1 ¯ max Ω ^ 3 ) ̲ min ( Ω ^ 2 ¯ max Ω ^ 3 ) .

  3. ( Ω ^ 1 ¯ max Ω ^ 2 ) ̲ min Ω ^ 3 = ( Ω ^ 1 ̲ min Ω ^ 3 ) ¯ max ( Ω ^ 2 ̲ min Ω ^ 3 ) .

  4. ( Ω ^ 1 ¯ min Ω ^ 2 ) ̲ max Ω ^ 3 = ( Ω ^ 1 ̲ max Ω ^ 3 ) ¯ min ( Ω ^ 2 ̲ max Ω ^ 3 ) .

Proof

The first part only has to be proven; the rest are analogous.

(1)

( Ω ^ 1 ̲ max Ω ^ 2 ) ¯ min Ω ^ 3 = ( max { μ ¯ 1 , μ ¯ 2 } , min { μ ̲ 1 , μ ̲ 2 } ; max { ρ ^ 1 , ρ ^ 2 } ) ¯ min ( μ ¯ 3 , μ ̲ 3 ; ρ ^ 3 ) = ( min { max { μ ¯ 1 , μ ¯ 2 } , μ ¯ 3 } , max { min { μ ̲ 1 , μ ̲ 2 } , μ ̲ 3 } ; min { max { ρ ^ 1 , ρ ^ 2 } , ρ ^ 3 } ) = ( max { min { μ ¯ 1 , μ ¯ 3 } , min { μ ¯ 2 , μ ¯ 3 } } , min { max { μ ̲ 1 , μ ̲ 3 } , max { μ ̲ 2 , μ ̲ 3 } } ; max { min { ρ ^ 1 , ρ ^ 3 } , min { ρ ^ 2 , ρ ^ 3 } } ) = ( min { μ ¯ 1 , μ ¯ 3 } , max { μ ̲ 1 , μ ̲ 3 } ; min { ρ ^ 1 , ρ ^ 3 } ) ̲ max ( min { μ ¯ 2 , μ ¯ 3 } , max { μ ̲ 2 , μ ̲ 3 } ; min { ρ ^ 2 , ρ ^ 3 } ) = ( Ω ^ 1 ¯ min Ω ^ 3 ) ̲ max ( Ω ^ 2 ¯ min Ω ^ 3 ) .

Theorem 3.17

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) , Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) and Ω ^ 3 = ( μ ¯ Ω ^ 3 , μ ̲ Ω ^ 3 ; ρ ^ 3 ) be three Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ̲ max ( Ω ^ 2 max Ω ^ 3 ) .

  2. ( Ω ^ 1 ̲ min Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ̲ min ( Ω ^ 2 max Ω ^ 3 ) .

  3. ( Ω ^ 1 ̲ max Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ̲ max ( Ω ^ 2 min Ω ^ 3 ) .

  4. ( Ω ^ 1 ̲ min Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ̲ min ( Ω ^ 2 min Ω ^ 3 ) .

  5. ( Ω ^ 1 ¯ max Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ¯ max ( Ω ^ 2 max Ω ^ 3 ) .

  6. ( Ω ^ 1 ¯ min Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ¯ min ( Ω ^ 2 max Ω ^ 3 ) .

  7. ( Ω ^ 1 ¯ max Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ¯ max ( Ω ^ 2 min Ω ^ 3 ) .

  8. ( Ω ^ 1 ¯ min Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ¯ min ( Ω ^ 2 min Ω ^ 3 ) .

Proof

The first part only has to be proven; the rest are analogous.

(1)

( Ω ^ 1 ̲ min Ω ^ 2 ) max Ω ^ 3 = ( max { μ ¯ Ω ^ 1 , μ ¯ Ω ^ 2 } , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } ; min { ρ ^ 1 , ρ ^ 2 } ) max ( μ ¯ Ω ^ 3 , μ ̲ Ω ^ 3 ; ρ ^ 3 ) = ( max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } + μ ¯ Ω ^ 3 n max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } μ ¯ Ω ^ 3 n n , min { μ ̲ Ω ^ 1 , μ ̲ Ω ^ 2 } μ ̲ Ω ^ 3 ; max { min { ρ ^ 1 , ρ ^ 2 } , ρ ^ 3 } ) = ( ( 1 μ ¯ Ω ^ 3 n ) max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } + μ ¯ Ω ^ 3 n n , min { μ ̲ Ω ^ 1 μ ̲ Ω ^ 3 , μ ̲ Ω ^ 2 μ ̲ Ω ^ 3 } ; min { max { ρ ^ 2 , ρ ^ 3 } , max { ρ ^ 1 , ρ ^ 3 } } ) ,

and

( Ω ^ 1 max Ω ^ 3 ) ̲ min ( Ω ^ 2 max Ω ^ 3 ) = ( μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 3 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 3 n n , μ ̲ Ω ^ 1 μ ̲ Ω ^ 3 ; max { ρ ^ 1 , ρ ^ 3 } ) ̲ min ( μ ¯ Ω ^ 2 n + μ ¯ Ω ^ 3 n μ ¯ Ω ^ 2 n μ ¯ Ω ^ 3 n n , μ ̲ Ω ^ 2 μ ̲ Ω ^ 3 ; max { ρ ^ 2 , ρ ^ 3 } ) = max { μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 3 n μ ¯ Ω ^ 1 n μ ¯ Ω ^ 3 n n , μ ¯ Ω ^ 2 n + μ ¯ Ω ^ 3 n μ ¯ Ω ^ 2 n μ ¯ Ω ^ 3 n n } , min { μ ̲ Ω ^ 1 μ ̲ Ω ^ 3 , μ ̲ Ω ^ 2 μ ̲ Ω ^ 3 } ; min { max { ρ ^ 1 , ρ ^ 3 } , max { ρ ^ 2 , ρ ^ 3 } } ) = max { 1 μ ¯ Ω ^ 3 n μ ¯ Ω ^ 1 n + μ ¯ Ω ^ 3 n , 1 μ ¯ Ω ^ 3 n μ ¯ Ω ^ 2 n + μ ¯ Ω ^ 3 n } , min { μ ̲ Ω ^ 1 μ ̲ Ω ^ 3 , μ ̲ Ω ^ 2 μ ̲ Ω ^ 3 } ; min { max { ρ ^ 1 , ρ ^ 3 } , max { ρ ^ 2 , ρ ^ 3 } } ) = ( ( 1 μ ¯ Ω ^ 3 n ) max { μ ¯ Ω ^ 1 n , μ ¯ Ω ^ 2 n } + μ ¯ Ω ^ 3 n , min { μ ̲ Ω ^ 1 μ ̲ Ω ^ 3 , μ ̲ Ω ^ 2 μ ̲ Ω ^ 3 } ; min { max { ρ ^ 1 , ρ ^ 2 } , max { ρ ^ 1 , ρ ^ 3 } } ) = ( Ω ^ 1 ̲ min Ω ^ 2 ) max Ω ^ 3 .

Theorem 3.18

Let Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) , Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) and Ω ^ 3 = ( μ ¯ Ω ^ 3 , μ ̲ Ω ^ 3 ; ρ ^ 3 ) be three Cn,m-ROFVs. Then,

  1. ( Ω ^ 1 ̲ max Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ̲ max ( Ω ^ 2 max Ω ^ 3 ) .

  2. ( Ω ^ 1 ̲ min Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ̲ min ( Ω ^ 2 max Ω ^ 3 ) .

  3. ( Ω ^ 1 ̲ max Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ̲ max ( Ω ^ 2 min Ω ^ 3 ) .

  4. ( Ω ^ 1 ̲ min Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ̲ min ( Ω ^ 2 min Ω ^ 3 ) .

  5. ( Ω ^ 1 ¯ max Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ¯ max ( Ω ^ 2 max Ω ^ 3 ) .

  6. ( Ω ^ 1 ¯ min Ω ^ 2 ) max Ω ^ 3 = ( Ω ^ 1 max Ω ^ 3 ) ¯ min ( Ω ^ 2 max Ω ^ 3 ) .

  7. ( Ω ^ 1 ¯ max Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ¯ max ( Ω ^ 2 min Ω ^ 3 ) .

  8. ( Ω ^ 1 ¯ min Ω ^ 2 ) min Ω ^ 3 = ( Ω ^ 1 min Ω ^ 3 ) ¯ min ( Ω ^ 2 min Ω ^ 3 ) .

Proof

We can prove that in a way that is analogous to Theorem 3.17.□

4 Circular n,m-ROF aggregation operators

This section includes a detailed indication of some interesting aspects as well as our proposal for four novel aggregation techniques on circular n,m-ROFS.

Definition 4.1

Assume that κ = ( κ 1 , κ 2 , , κ α ) is a vector of weights such that i = 1 α κ i = 1 with κ i > 0 for all i , and Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) is a Cn,m-ROFVs for each i = 1 , , α . Then,

  1. The Cn,m-ROFWAmax and Cn,m-ROFWAmin operators are the mappings Cn,m-ROFWAmax ( C n , m -ROFWA min ) : Ω ^ α Ω ^ , such that

    C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = κ 1 Ω ^ 1 max κ 2 Ω ^ 2 max max κ α Ω ^ α ,

    and

    C n , m -ROFWA min ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = κ 1 Ω ^ 1 min κ 2 Ω ^ 2 min min κ α Ω ^ α .

  2. The Cn,m-ROFWGmax and Cn,m-ROFWGmin operators are the mappings Cn,m-ROFWGmax ( C n , m -ROFWG min ) : Ω ^ α Ω ^ , such that

    C n , m -ROFWG max ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = Ω ^ 1 κ 1 max Ω ^ 2 κ 2 max max Ω ^ α κ α ,

    and

    C n , m -ROFWG min ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = Ω ^ 1 κ 1 min Ω ^ 2 κ 2 min min Ω ^ α κ α .

Theorem 4.2

Assume that κ = ( κ 1 , κ 2 , , κ α ) is a vector of weights such that i = 1 α κ i = 1 with κ i > 0 for all i , and Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) is a Cn,m-ROFVs for each i = 1 , , α . Then, another way to write the Cn,m-ROFWAmax and Cn,m-ROFWAmin operators is as follows:

  1. C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) = ( 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , , ρ ^ α } ) .

  2. C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) = ( 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i ; min { ρ ^ 1 , , ρ ^ α } ) .

Proof

(1) Mathematical induction can be used to illustrate this result. For α = 2 , we have

C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 ) = κ 1 Ω ^ 1 max κ 2 Ω ^ 2 = ( 1 ( 1 μ ¯ Ω ^ 1 n ) κ 1 n , μ ̲ Ω ^ 1 κ 1 ; ρ ^ 1 ) max ( 1 ( 1 μ ¯ Ω ^ 2 n ) κ 2 n , μ ̲ Ω ^ 2 κ 2 ; ρ ^ 2 ) = ( 1 ( 1 μ ¯ Ω ^ 1 n ) κ 1 + 1 ( 1 μ ¯ Ω ^ 2 n ) κ 2 ( 1 ( 1 μ ¯ Ω ^ 1 n ) κ 1 ) ( 1 ( 1 μ ¯ Ω ^ 2 n ) κ 2 ) 1 n , μ ̲ Ω ^ 1 κ 1 μ ̲ Ω ^ 2 κ 2 ; max { ρ ^ 1 , ρ ^ 2 } ) = 1 i 2 ( 1 μ ¯ Ω ^ i n ) κ i n , i 2 μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , ρ ^ 2 } .

Assuming α = k is true, this means

C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ k ) = 1 i k ( 1 μ ¯ Ω ^ i n ) κ i n , i k μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , , ρ ^ k } .

We have to demonstrate that α = k + 1 is true.

1 i k ( 1 μ ¯ Ω ^ i n ) κ i n , i k μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , , ρ ^ k } max ( 1 ( 1 μ ¯ Ω ^ k + 1 n ) κ k + 1 n , μ ̲ Ω ^ k + 1 κ k + 1 ; ρ ^ k + 1 ) = 1 i k + 1 ( 1 μ ¯ Ω ^ i n ) κ i n , i k + 1 μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , ρ ^ 2 , , ρ ^ k + 1 } .

(2) The proof is analogous to (1).□

Theorem 4.3

Assume that κ = ( κ 1 , κ 2 , , κ α ) is a vector of weights such that i = 1 α κ i = 1 with κ i > 0 for all i , and Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) is a Cn,m-ROFVs for each i = 1 , , α . Then, another way to write the Cn,m-ROFWGmax and Cn,m-ROFWGmin operators is as follows:

  1. C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) = i α μ ¯ Ω ^ i κ i , 1 i α ( 1 μ ̲ Ω ^ i m ) κ i m ; max { ρ ^ 1 , , ρ ^ α } .

  2. C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) = i α μ ¯ Ω ^ i κ i , 1 i α ( 1 μ ̲ Ω ^ i m ) κ i m ; min { ρ ^ 1 , , ρ ^ α } .

Proof

The proof is similar to the proof of Theorem 4.2.□

Example 4.4

Let Ω ^ 1 = ( 0.9 , 0.8 ; 0.05 ) , Ω ^ 2 = ( 0.8 , 0.9 ; 0.03 ) , and Ω ^ 3 = ( 0.7 , 0.7 ; 0.08 ) , be the three Cn,m-ROFVs with weight vector κ = ( 0.4 , 0.1 , 0.5 ) T , respectively, then

(1)

C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) = 1 i 3 ( 1 μ ¯ Ω ^ i n ) κ i n , i 3 μ ̲ Ω ^ i κ i ; max { 0.05 , 0.03 , 0.08 } ( 0.8301 , 0.7572 ; 0.08 ) for  n = 6  and  m = 4 , ( 0.8206 , 0.7572 ; 0.08 ) for  n = 3  and  m = 7 , ( 0.8269 , 0.7572 ; 0.08 ) for  n = 5  and  m = 6 .

(2)

C n , m -ROFWA min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) = 1 i 3 ( 1 μ ¯ Ω ^ i n ) κ i n , i 3 μ ̲ Ω ^ i κ i ; min { 0.05 , 0.03 , 0.08 } ( 0.8301 , 0.7572 ; 0.03 ) for  n = 6  and  m = 4 , ( 0.8206 , 0.7572 ; 0.03 ) for  n = 3  and  m = 7 , ( 0.8269 , 0.7572 ; 0.03 ) for  n = 5  and  m = 6 .

(3)

C n , m -ROFWG max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) = i 3 μ ¯ Ω ^ i κ i , 1 i 3 ( 1 μ ̲ Ω ^ i m ) κ i m ; max { 0.05 , 0.03 , 0.08 } ( 0.7844 , 0.7775 ; 0.08 ) for  n = 6  and  m = 4 , ( 0.7844 , 0.7846 ; 0.08 ) for  n = 3  and  m = 7 , ( 0.7844 , 0.7821 ; 0.08 ) for  n = 5  and  m = 6 .

(4)

C n , m -ROFWG min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) = i 3 μ ¯ Ω ^ i κ i , 1 i 3 ( 1 μ ̲ Ω ^ i m ) κ i m ; min { 0.05 , 0.03 , 0.08 } ( 0.7844 , 0.7775 ; 0.03 ) for  n = 6  and  m = 4 , ( 0.7844 , 0.7846 ; 0.03 ) for  n = 3  and  m = 7 , ( 0.7844 , 0.7821 ; 0.03 ) for  n = 5  and  m = 6 .

Theorem 4.5

(Monotonicity) Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α and { Ω ˜ i = ( μ ¯ Ω ˜ i , μ ̲ Ω ˜ i ; ρ ˜ i ) } i = 1 , , α be two lists of α Cn,m-ROFVs. If Ω ^ i Ω ˜ i for all i , then

  1. C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) C n , m -ROFWA max ( Ω ˜ 1 , , Ω ˜ α ) .

  2. C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) C n , m -ROFWA min ( Ω ˜ 1 , , Ω ˜ α ) .

  3. C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) C n , m -ROFWG max ( Ω ˜ 1 , , Ω ˜ α ) .

  4. C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) C n , m -ROFWG min ( Ω ˜ 1 , , Ω ˜ α ) .

Proof

The first part only has to be proven; the rest are analogous. Since for all i , we have μ ¯ Ω ^ i μ ¯ Ω ˜ i , μ ̲ Ω ^ i μ ̲ Ω ˜ i and ρ ^ i ρ ˜ i , then

1 i = 1 α ( 1 ( μ ¯ Ω ^ i ) n ) κ i 1 n 1 i = 1 α ( 1 ( μ ¯ Ω ˜ i ) n ) κ i 1 n ,

and

i = 1 α ( μ ̲ Ω ˜ i ) κ i i = 1 α ( μ ̲ Ω ^ i ) κ i .

Thus,

C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = 1 i = 1 α ( 1 ( μ ¯ Ω ^ i ) n ) κ i 1 n , i = 1 α ( μ ̲ Ω ^ i ) κ i ; max { ρ ^ 1 , , ρ ^ α } 1 i = 1 α ( 1 ( μ ¯ Ω ˜ i ) n ) κ i 1 n , i = 1 α ( μ ̲ Ω ˜ i ) κ i ; max { ρ ˜ 1 , , ρ ˜ α } = C n , m -ROFWA max ( Ω ˜ 1 , Ω ˜ 2 , , Ω ˜ α ) .

Theorem 4.6

(Boundedness) Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs. If Ω ̲ and Ω ¯ are two Cn,m-ROFVs such that

Ω ̲ = ( μ ¯ Ω ̲ , μ ̲ Ω ̲ ; ρ ̲ ) = ( min ( μ ¯ Ω ^ i ) , max ( μ ̲ Ω ^ i ) ; min ( ρ ^ i ) )

and

Ω ¯ = ( μ ¯ Ω ¯ , μ ̲ Ω ¯ , ρ ¯ ) = ( max ( μ ¯ Ω ^ i ) , min ( μ ̲ Ω ^ i ) ; max ( ρ ^ i ) ) ,

then

  1. Ω ̲ C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) Ω ¯ .

  2. Ω ̲ C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) Ω ¯ .

  3. Ω ̲ C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) Ω ¯ .

  4. Ω ̲ C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) Ω ¯ .

Proof

The first part only has to be proven; the rest are analogous. To finish the first part, we must demonstrate that μ ¯ Ω ̲ 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n μ ¯ Ω ¯ , μ ̲ Ω ̲ i α μ ̲ Ω ^ i κ i μ ̲ Ω ¯ , and ρ ̲ max { ρ ^ 1 , , ρ ^ α } ρ ¯ .

Since μ ¯ Ω ̲ n μ ¯ Ω ^ i n μ ¯ Ω ¯ n , we have

i α ( 1 μ ¯ Ω ̲ n ) κ i i α ( 1 μ ¯ Ω ^ i n ) κ i i α ( 1 μ ¯ Ω ¯ n ) κ i ,

and hence,

( 1 μ ¯ Ω ̲ n ) i α κ i i α ( 1 μ ¯ Ω ^ i n ) κ i ( 1 μ ¯ Ω ¯ n ) i α κ i

since i α κ i = 1 , so

μ ¯ Ω ̲ 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n μ ¯ Ω ¯ .

In the same way, we can demonstrate μ ̲ Ω ̲ i α μ ̲ Ω ^ i κ i μ ̲ Ω ¯ , and ρ ̲ max { ρ ^ 1 , , ρ ^ α } ρ ¯ .□

Theorem 4.7

(Idempotency) Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs such that Ω ^ i = Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) . If κ = ( κ 1 , κ 2 , , κ α ) is a weight vector with i = 1 α κ i = 1 and κ > 0 , then

  1. C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) = Ω ^ .

  2. C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) = Ω ^ .

  3. C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) = Ω ^ .

  4. C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) = Ω ^ .

Proof

The first part only has to be proven; the rest are analogous. Since Ω ^ i = Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) ( i = 1 , 2 , , α ) , then

C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) = ( 1 i = 1 α ( 1 ( μ ¯ Ω ^ i ) n ) κ i ) 1 n , i = 1 α ( μ ̲ Ω ^ i ) κ i ; max { ρ ^ 1 , , ρ ^ α } = ( 1 i = 1 α ( 1 ( μ ¯ Ω ^ ) n ) κ i ) 1 n , i = 1 α ( μ ̲ Ω ^ ) κ i ; max { ρ ^ , , ρ ^ } = ( 1 ( 1 ( μ ¯ Ω ^ ) n ) i = 1 α κ i ) 1 n , ( μ ̲ Ω ^ ) i = 1 α κ i ; ρ ^ = ( 1 ( 1 ( μ ¯ Ω ^ ) n ) ) 1 n , ( μ ̲ Ω ^ ) ; ρ ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) = Ω ^ .

Theorem 4.8

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs and Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) be any Cn,m-ROFV. If κ = ( κ 1 , , κ α ) is a weight vector with i = 1 α κ i = 1 , then

  1. C n , m -ROFWA max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWA max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) .

  2. C n , m -ROFWA min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWA min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) .

  3. C n , m -ROFWA min ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWA min ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) .

  4. C n , m -ROFWA max ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWA max ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) .

  5. C n , m -ROFWG max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWG max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) .

  6. C n , m -ROFWG min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWG min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) .

  7. C n , m -ROFWG min ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWG min ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) .

  8. C n , m -ROFWG max ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWG max ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) .

Proof

For any Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) and Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) , we have

μ ¯ Ω ^ i n ( ζ ) μ ¯ Ω ^ i n ( ζ ) μ ¯ Ω ^ n ( ζ )

and

μ ¯ Ω ^ n ( ζ ) μ ¯ Ω ^ i n ( ζ ) μ ¯ Ω ^ n ( ζ ) ,

so

μ ¯ Ω ^ n ( ζ ) μ ¯ Ω ^ i n ( ζ ) μ ¯ Ω ^ n ( ζ ) 0 ,

and hence,

μ ¯ Ω ^ i n ( ζ ) + μ ¯ Ω ^ n ( ζ ) μ ¯ Ω ^ i n ( ζ ) μ ¯ Ω ^ n ( ζ ) n μ ¯ Ω ^ i ( ζ ) μ ¯ Ω ^ ( ζ ) .

Similarly, we have

μ ̲ Ω ^ i m ( ζ ) + μ ̲ Ω ^ m ( ζ ) μ ̲ Ω ^ i m ( ζ ) μ ̲ Ω ^ m ( ζ ) m μ ̲ Ω ^ i ( ζ ) μ ̲ Ω ^ ( ζ ) .

Thus, Ω ^ i max Ω ^ Ω ^ i max Ω ^ and Ω ^ i min Ω ^ Ω ^ i min Ω ^ . Hence, Theorem 4.5 makes it easy to derive the proofs of all parts.□

Theorem 4.9

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs and Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) be any Cn,m-ROFV. If κ = ( κ 1 , κ 2 , , κ α ) is a weight vector with i = 1 α κ i = 1 , then

  1. C n , m -ROFWA max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ .

  2. C n , m -ROFWA min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ .

  3. C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ .

  4. C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ .

  5. C n , m -ROFWG max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ .

  6. C n , m -ROFWG min ( Ω ^ 1 min Ω ^ , , Ω ^ α min Ω ^ ) C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ .

  7. C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) max Ω ^ .

  8. C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) min Ω ^ .

Proof

Since Ω ^ i max Ω ^ = ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n n , μ ̲ Ω ^ i μ ̲ Ω ^ ; max { ρ ^ i , ρ ^ } ) . Then,

C n , m -ROFWA max ( Ω ^ 1 max Ω ^ , , Ω ^ α max Ω ^ ) = 1 i α ( 1 ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n n ) n ) κ i n , i α ( μ ̲ Ω ^ i μ ̲ Ω ^ ) κ i ; max { ρ ^ 1 , , ρ ^ α , ρ ^ } .

And also,

C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , , Ω ^ α ) max Ω ^ = 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , , ρ ^ α } max ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) = μ ¯ Ω ^ 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i m + μ ̲ Ω ^ m i α μ ̲ Ω ^ i κ i m μ ̲ Ω ^ m m ; max { ρ ^ 1 , , ρ ^ α , ρ ^ } .

We have to prove that

1 i α ( 1 ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n n ) n ) κ i n μ ¯ Ω ^ 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n ,

and

i α μ ̲ Ω ^ i κ i m + μ ̲ Ω ^ m i α μ ̲ Ω ^ i κ i m μ ̲ Ω ^ m m i α ( μ ̲ Ω ^ i μ ̲ Ω ^ ) κ i .

These expressions need to be verified.

Since,

μ ¯ Ω ^ i n μ ¯ Ω ^ n μ ¯ Ω ^ n ,

then

1 ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n ) 1 μ ¯ Ω ^ i n ,

and hence,

i α ( 1 ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n ) ) κ i i α ( 1 μ ¯ Ω ^ i n ) κ i ,

therefore,

1 i α ( 1 ( μ ¯ Ω ^ i n + μ ¯ Ω ^ n μ ¯ Ω ^ i n μ ¯ Ω ^ n ) ) κ i μ ¯ Ω ^ n 1 i α ( 1 μ ¯ Ω ^ i n ) κ i ,

this is generally correct. Since

μ ̲ Ω ^ i κ i μ ̲ Ω ^ i κ i μ ̲ Ω ^ κ i ,

then

i α μ ̲ Ω ^ i κ i m i α ( μ ̲ Ω ^ i μ ̲ Ω ^ ) κ i m ,

and hence,

i α μ ̲ Ω ^ i κ i m + μ ̲ Ω ^ m i α μ ̲ Ω ^ i κ i m μ ̲ Ω ^ m m i α ( μ ̲ Ω ^ i μ ̲ Ω ^ ) κ i .

Thus, the first part is proven. In a similar manner, the other parts can be proven.□

Theorem 4.10

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs. If κ = ( κ 1 , κ 2 , , κ α ) be the weight vector of Ω ^ i with i = 1 α κ i = 1 and ε > 0 , then

  1. C n , m -ROFWA max ( ε Ω ^ 1 , , ε Ω ^ α ) C n , m -ROFWA max ( Ω ^ 1 ε , , Ω ^ α ε ) .

  2. C n , m -ROFWA min ( ε Ω ^ 1 , , ε Ω ^ α ) C n , m -ROFWA min ( Ω ^ 1 ε , , Ω ^ α ε ) .

  3. ε C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) ( C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) ) ε .

  4. ε C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) ( C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) ) ε .

  5. C n , m -ROFWG max ( ε Ω ^ 1 , , ε Ω ^ α ) C n , m -ROFWG max ( Ω ^ 1 ε , , Ω ^ α ε ) .

  6. C n , m -ROFWG min ( ε Ω ^ 1 , , ε Ω ^ α ) C n , m -ROFWG min ( Ω ^ 1 ε , , Ω ^ α ε ) .

  7. ε C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) ( C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) ) ε .

  8. ε C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) ( C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) ) ε .

Proof

Since for all i , we have

ε Ω ^ i = ( 1 ( 1 μ ¯ Ω ^ i n ) ε n , μ ̲ Ω ^ i ε ; ρ ^ ) ,

and

Ω ^ i ε = ( μ ¯ Ω ^ i ε , 1 ( 1 μ ̲ Ω ^ i m ) ε m ; ρ ^ ) .

The Newton generalized binomial theorem now allows us to obtain

( 1 μ ¯ Ω ^ i n ) ε + ( μ ¯ Ω ^ i n ) ε ( 1 μ ¯ Ω ^ i n + μ ¯ Ω ^ i n ) ε = 1 ,

so

1 ( 1 μ ¯ Ω ^ i n ) ε ( μ ¯ Ω ^ i ε ) n ,

implies that

1 ( 1 μ ¯ Ω ^ i n ) ε n μ ¯ Ω ^ i ε .

Similarly,

1 ( 1 μ ̲ Ω ^ i m ) ε m μ ̲ Ω ^ i ε .

Therefore, Ω ^ i ε ε Ω ^ i . Therefore, it is simple to deduce the proofs of every part from the Theorem 4.5.□

Theorem 4.11

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α and { Ω ˜ i = ( μ ¯ Ω ˜ i , μ ̲ Ω ˜ i ; ρ ˜ i ) } i = 1 , , α be two lists of α Cn,m-ROFVs. If κ = ( κ 1 , , κ α ) is a weight vector with i = 1 α κ i = 1 , then

  1. C n , m -ROFWA max ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) C n , m -ROFWA max ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) .

  2. C n , m -ROFWA max ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) C n , m -ROFWA max ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) .

  3. C n , m -ROFWA min ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) C n , m -ROFWA min ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) .

  4. C n , m -ROFWA min ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) C n , m -ROFWA min ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) .

  5. C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWA max ( Ω ˜ 1 , , Ω ˜ α ) C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWA max ( Ω ˜ 1 , , Ω ˜ α ) .

  6. C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWA min ( Ω ˜ 1 , , Ω ˜ α ) C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWA min ( Ω ˜ 1 , , Ω ˜ α ) .

Proof

Since for any Cn,m-ROFVs Ω ^ i and Ω ˜ i , we have

Ω ^ i max Ω ˜ i Ω ^ i max Ω ˜ i and Ω ^ i min Ω ˜ i Ω ^ i min Ω ˜ i .

Therefore, it is simple to deduce the proofs of every part from the Theorem 4.5.□

Theorem 4.12

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α and { Ω ˜ i = ( μ ¯ Ω ˜ i , μ ̲ Ω ˜ i ; ρ ˜ i ) } i = 1 , , α be two lists of α Cn,m-ROFVs. If κ = ( κ 1 , , κ α ) is a weight vector with i = 1 α κ i = 1 , then

  1. C n , m -ROFWG max ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) C n , m -ROFWG max ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) .

  2. C n , m -ROFWG max ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) C n , m -ROFWG max ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) .

  3. C n , m -ROFWG min ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) C n , m -ROFWG min ( Ω ^ 1 max Ω ˜ 1 , , Ω ^ α max Ω ˜ α ) .

  4. C n , m -ROFWG min ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) C n , m -ROFWG min ( Ω ^ 1 min Ω ˜ 1 , , Ω ^ α min Ω ˜ α ) .

  5. C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWG max ( Ω ˜ 1 , , Ω ˜ α ) C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWG max ( Ω ˜ 1 , , Ω ˜ α ) .

  6. C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWG min ( Ω ˜ 1 , , Ω ˜ α ) C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) max C n , m -ROFWG min ( Ω ˜ 1 , , Ω ˜ α ) .

Proof

The proof is similar to the proof of Theorem 4.11.□

Theorem 4.13

Let { Ω ^ i = ( μ ¯ Ω ^ i , μ ̲ Ω ^ i ; ρ ^ i ) } i = 1 , , α be a list of α Cn,m-ROFVs. If κ = ( κ 1 , κ 2 , , κ α ) is a weight vector with i = 1 α κ i = 1 , then

  1. C n , m -ROFWA max ( Ω ^ 1 c , , Ω ^ α c ) = ( C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) ) c .

  2. C n , m -ROFWA min ( Ω ^ 1 c , , Ω ^ α c ) = ( C n , m -ROFWG min ( Ω ^ 1 , , Ω ^ α ) ) c .

  3. C n , m -ROFWG max ( Ω ^ 1 c , , Ω ^ α c ) = ( C n , m -ROFWA max ( Ω ^ 1 , , Ω ^ α ) ) c .

  4. C n , m -ROFWG min ( Ω ^ 1 c , , Ω ^ α c ) = ( C n , m -ROFWA min ( Ω ^ 1 , , Ω ^ α ) ) c .

Proof

The first part only has to be proven; the rest are analogous.

(1) Since for all i , we have

C n , m -ROFWA max ( Ω ^ 1 c , Ω ^ 2 c , , Ω ^ α c ) = κ 1 Ω ^ 1 c max κ 2 Ω ^ 2 c max max κ α Ω ^ α c = ( Ω ^ 1 κ 1 ) c max ( Ω ^ 2 κ 2 ) c max max ( Ω ^ α κ α ) c = ( Ω ^ 1 κ 1 max Ω ^ 2 κ 2 max max Ω ^ α κ α ) c = ( C n , m -ROFWG max ( Ω ^ 1 , , Ω ^ α ) ) c .

We present some functions that are crucial for the ranking of Cn,m-ROFVs.

Definition 4.14

For any Cn,m-ROFV Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) , the

  1. Score function of Ω ^ is outlined thereby:

    S ^ ( Ω ^ ) = ( μ ¯ Ω ^ ) n ( μ ̲ Ω ^ ) m + ρ ^ 2 2 .

  2. Accuracy function of Ω ^ is outlined thereby:

    A ^ ( Ω ^ ) = ( μ ¯ Ω ^ ) n + ( μ ̲ Ω ^ ) m + ρ ^ 2 2 .

Example 4.15

Consider Example 4.4, then

  1. S ^ ( C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.0550 for  n = 6   and  m = 4 , 0.4663 for   n = 3  and  m = 7 , 0.2547 for  n = 5  and  m = 6 ,

    and A ^ ( C n , m -ROFWA max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.7124 for  n = 6  and  m = 4 , 0.7518 for  n = 3  and  m = 7 , 0.6317 for  n = 5  and  m = 6 .

  2. S ^ ( C n , m -ROFWA min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.0196 for  n = 6  and  m = 4 , 0.4310 for  n = 3  and  m = 7 , 0.2194 for  n = 5  and  m = 6 ,

    and A ^ ( C n , m -ROFWA min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.6771 for  n = 6  and  m = 4 , 0.7164 for  n = 3  and  m = 7 , 0.5963 for  n = 5  and  m = 6 .

  3. S ^ ( C n , m -ROFWG max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.0759 for  n = 6  and  m = 4 , 0.3562 for  n = 3  and  m = 7 , 0.1246 for  n = 5  and  m = 6 ,

    and A ^ ( C n , m -ROFWG max ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.6550 for  n = 6  and  m = 4 , 0.7223 for  n = 3  and  m = 7 , 0.5825 for  n = 5  and  m = 6 .

  4. S ^ ( C n , m -ROFWG min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.1112 for  n = 6  and  m = 4 , 0.3208 for  n = 3  and  m = 7 , 0.0893 for  n = 5  and  m = 6 ,

    and A ^ ( C n , m -ROFWG min ( Ω ^ 1 , Ω ^ 2 , Ω ^ 3 ) )

    0.6196 for  n = 6  and  m = 4 , 0.6870 for  n = 3  and  m = 7 , 0.5472 for  n = 5  and  m = 6 .

Remark 4.16

For any Cn,m-ROFV Ω ^ = ( μ ¯ Ω ^ , μ ̲ Ω ^ ; ρ ^ ) , the suggested

  1. score function S ^ ( Ω ^ ) [ 1 , 2 ] .

  2. accuracy function A ^ ( Ω ^ ) [ 0 , 2 ] .

Definition 4.17

For any two Cn,m-ROFVs Ω ^ 1 = ( μ ¯ Ω ^ 1 , μ ̲ Ω ^ 1 ; ρ ^ 1 ) and Ω ^ 2 = ( μ ¯ Ω ^ 2 , μ ̲ Ω ^ 2 ; ρ ^ 2 ) , the alleged comparison approach as follows:

  1. if S ^ ( Ω ^ 1 ) < S ^ ( Ω ^ 2 ) , then Ω ^ 1 Ω ^ 2 ,

  2. if S ^ ( Ω ^ 1 ) > S ^ ( Ω ^ 2 ) , then Ω ^ 1 Ω ^ 2 ,

  3. if S ^ ( Ω ^ 1 ) = S ^ ( Ω ^ 2 ) , then

    1. if A ^ ( Ω ^ 1 ) < A ^ ( Ω ^ 2 ) , then Ω ^ 1 Ω ^ 2 ,

    2. if A ^ ( Ω ^ 1 ) > A ^ ( Ω ^ 2 ) , then Ω ^ 1 Ω ^ 2 ,

    3. if A ^ ( Ω ^ 1 ) = A ^ ( Ω ^ 2 ) , then Ω ^ 1 Ω ^ 2 .

Example 4.18

Let Ψ = { ζ } , then Ω ^ 1 = ( ζ , 0.6 , 0.7 ; 0.04 ) and Ω ^ 2 = ( ζ , 0.9 , 0.6 ; 0.06 ) are two C5,2-ROFVs, and so S ^ ( Ω ^ 1 ) = 0.3840 and S ^ ( Ω ^ 2 ) = 0.2729 , that is, Ω ^ 1 Ω ^ 2 .

5 Decision making on Cn,m-ROFVs

The provided operators are utilized in this section to construct an algorithm for MCDM based on the circular n,m-ROF conditions.

Suppose V e ^ = { v e 1 , v e 2 , , v e z } and T e ^ = { t e 1 , t e 2 , , t e α } are two finite collections of z alternatives and α attributes, respectively, for an MCDM issue. Let D ^ = [ Ω ^ j i ] = [ ( μ ¯ Ω ^ j i , μ ̲ Ω ^ j i ; ρ ^ j i ) ] z × α represent the DM that the decision maker has supplied, where each alternative v e j ( j = 1 , 2 , , z ) assessment data on attribute t e i ( i = 1 , 2 , , α ) is represented by ( μ ¯ Ω ^ j i , μ ̲ Ω ^ j i ; ρ ^ j i ) collection of Cn,m-ROFVs (Table 2). Let ε = ( ε 1 , ε 2 , , ε k ) T be the attribute’s weight vector where ε i > 0 and i = 1 α ε i = 1 . Since the decision-maker assigns membership and nonmembership values based on a circle with radius ρ ^ i , our goal is to represent the area of radius ρ ^ i by taking the optimistic views of the decision maker and placing them in a representative space with radius ρ ^ i . Afterward, the following is a presentation of the primary method (Algorithm 1) for solving the MCDM problems using the suggested circular n,m-ROF aggregating operators:

Table 2

Circular n,m-ROF decision table

Alternative/criteria t e 1 t e 2 t e α
v e 1 ( μ ¯ Ω ^ 11 , μ ̲ Ω ^ 11 ; ρ ^ 11 ) ( μ ¯ Ω ^ 12 , μ ̲ Ω ^ 12 ; ρ ^ 12 ) ( μ ¯ Ω ^ 1 α , μ ̲ Ω ^ 1 α ; ρ ^ 1 α )
v e 2 ( μ ¯ Ω ^ 21 , μ ̲ Ω ^ 21 ; ρ ^ 21 ) ( μ ¯ Ω ^ 22 , μ ̲ Ω ^ 22 ; ρ ^ 22 ) ( μ ¯ Ω ^ 2 α , μ ̲ Ω ^ 2 α ; ρ ^ 2 α )
. . . .
. . . .
. . . .
v e z ( μ ¯ Ω ^ z 1 , μ ̲ Ω ^ z 1 ; ρ ^ z 1 ) ( μ ¯ Ω ^ z 2 , μ ̲ Ω ^ z 2 ; ρ ^ z 2 ) ( μ ¯ Ω ^ z α , μ ̲ Ω ^ z α ; ρ ^ z α )
Algorithm 1:
Step 1. To build the application’s framework, identify the alternatives, pertinent criteria, and radius ρ ^ i .
Step 2. Create the circular n,m-ROF decision matrix D ^ = [ Ω ^ j i ] z × α using Cn,m-ROFVS.
Step 3. Construct a normalized circular n,m-ROF decision matrix by using the circular n,m-ROF decision matrix D ^ = [ Ω ^ j i ] z × α .
Step 4. Apply the following operators for evaluating alternative option values with corresponding weights: 1. VeA j max = C n , m -ROFWA max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j α ) = 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i ; max { ρ ^ 1 , , ρ ^ α } .
2. VeA j min = C n , m -ROFWA min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j α ) = 1 i α ( 1 μ ¯ Ω ^ i n ) κ i n , i α μ ̲ Ω ^ i κ i ; min { ρ ^ 1 , , ρ ^ α } .
3. VeG j max = C n , m -ROFWG max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j α ) = i α μ ¯ Ω ^ i κ i , 1 i α ( 1 μ ̲ Ω ^ i m ) κ i m ; max { ρ ^ 1 , , ρ ^ α } .
4. VeG j min = C n , m -ROFWG min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j α ) = i α μ ¯ Ω ^ i κ i , 1 i α ( 1 μ ̲ Ω ^ i m ) κ i m ; min { ρ ^ 1 , , ρ ^ α } .
For each j = 1 , 2 , , z .
Step 5. Compute the score values for the final circular n,m-ROFV of V e A j max , V e A j min , V e G j max , and V e G j min ( j = 1 , 2 , , z ) that were obtained in Step 4.
Step 6. The optimal ranking order of the options can be determined by applying Definition 4.17 to decide which is the optimal choice.

To facilitate a clearer understanding of Algorithm 1, a flowchart diagram is provided in Figure 2.

Figure 2 
               Flowchart of Algorithm 1.
Figure 2

Flowchart of Algorithm 1.

5.1 Application for selection a best teacher

To choose the best teacher, an MCDM problem is described in this section. When hiring new teachers, the administration of the school seeks out capable teachers who can make a significant impact in the academic performance of their students.

The following five teacher criteria ought to be taken into account while choosing teachers. The rationale for selecting these criteria is grounded in their alignment with the qualities that are widely recognized as essential for effective teaching and positive student outcomes:

  1. Passionate about learning ( t e 1 ):

  2. It is imperative that school administrators seek out teachers who have a strong enthusiasm for both teaching and learning. In addition to inspiring and influencing students’ accomplishments, teachers work tirelessly to give their charges the greatest education possible. The instructor isn’t hesitant to use cutting-edge resources and instructional strategies in their classes. A superb teacher is willing to discuss new strategies and approaches with their colleagues and is receptive to new ideas.

  3. Organizes and implements instruction ( t e 2 ):

  4. In the classroom, the teacher should be able to plan and carry out lessons. A good teacher must set goals, organize lessons, allot time, and have high standards for both the behavior and academic performance of their students. A professional educator will adapt their lessons to meet the needs of each individual student. The teacher needs to establish specific objectives for the students’ education and classroom activities.

  5. Skilled leader ( t e 3 ):

  6. To meet school accomplishment goals and support student learning, selectors should make an effort to learn about the skill sets of their teachers. A broad skill set is essential for an excellent teacher. Decision-making and teamwork are skills he or she possesses. It is imperative that educators possess both technical abilities and the ability to employ contemporary teaching and learning resources.

  7. Collaborates with colleagues on daily basis ( t e 4 ):

  8. The ability to work in a team and the teacher’s willingness to collaborate with other experts are important considerations for the selection. A great teacher asks for help and comments rather than worrying about how to improve themselves. The teacher should work together with other staff members at the school to develop as a team. Constructive criticism and suggestions from fellow educators can be advantageous for teachers. Everyone gains when experts collaborate with one another.

  9. Warm, accessible, enthusiastic, and caring ( t e 5 ):

  10. The interviewee’s previous school experiences should be considered by the school leader, who should ask a few questions. An excellent teacher is personable to all pupils, staff members, administrators, and other school personnel. If someone has any questions, complaints, or would just like to share a funny tale, they can obtain in touch with the teacher. An excellent listener is a necessary quality in a teacher.

Education is intricate and distinct for every student. In the classroom, there is not a single teaching strategy that suits every kid. The management should select teachers for the school based on their qualifications, experience, and understanding of child development, psychology, and other related fields.

To choose the best teacher, suppose there are ten teacher alternatives, marked by v e 1 , v e 2 , v e 3 , v e 4 , v e 5 , v e 6 , v e 7 , v e 8 , v e 9 , and v e 10 in this MCDM problem. The associated weight vector of the attributes is ε = ( 0.18 , 0.21 , 0.24 , 0.20 , 0.17 ) T , respectively. The DM [ Ω ^ j i ] 10 × 5 is presented in Table 3, where Ω ^ j i ( j = 1 , 2 , , 10 and i = 1 , 2 , , 5 ) are in the form of C3,2-ROFVs. At this point, the circular 3,2-ROFV DM can be computed as follows:

Table 3

Circular 3,2-ROF values

Teachers/criteria t e 1 t e 2 t e 3 t e 4 t e 5
v e 1 ( 0.7 , 0.6 ; 0.01 ) ( 0.2 , 0.5 ; 0.03 ) ( 0.9 , 0.4 ; 0.01 ) ( 0.8 , 0.2 ; 0.03 ) ( 0.1 , 0.9 ; 0.05 )
v e 2 ( 0.4 , 0.2 ; 0.07 ) ( 0.7 , 0.3 ; 0.01 ) ( 0.9 , 0.2 ; 0.08 ) ( 0.1 , 0.3 ; 0.01 ) ( 0.6 , 0.4 ; 0.02 )
v e 3 ( 0.3 , 0.4 ; 0.09 ) ( 0.8 , 0.1 ; 0.05 ) ( 0.7 , 0.1 ; 0.04 ) ( 0.9 , 0.3 ; 0.05 ) ( 0.2 , 0.1 ; 0.08 )
v e 4 ( 0.9 , 0.2 ; 0.09 ) ( 0.7 , 0.3 ; 0.05 ) ( 0.8 , 0.1 ; 0.05 ) ( 0.5 , 0.4 ; 0.09 ) ( 0.8 , 0.2 ; 0.08 )
v e 5 ( 0.8 , 0.6 ; 0.09 ) ( 0.3 , 0.7 ; 0.02 ) ( 0.9 , 0.2 ; 0.08 ) ( 0.1 , 0.6 ; 0.07 ) ( 0.4 , 0.7 ; 0.02 )
v e 6 ( 0.9 , 0.1 ; 0.01 ) ( 0.4 , 0.8 ; 0.01 ) ( 0.4 , 0.2 ; 0.04 ) ( 0.5 , 0.2 ; 0.06 ) ( 0.8 , 0.2 ; 0.03 )
v e 7 ( 0.6 , 0.7 ; 0.05 ) ( 0.1 , 0.9 ; 0.02 ) ( 0.9 , 0.2 ; 0.09 ) ( 0.3 , 0.4 ; 0.06 ) ( 0.8 , 0.3 ; 0.05 )
v e 8 ( 0.2 , 0.1 ; 0.06 ) ( 0.8 , 0.5 ; 0.08 ) ( 0.9 , 0.4 ; 0.09 ) ( 0.6 , 0.1 ; 0.05 ) ( 0.7 , 0.3 ; 0.03 )
v e 9 ( 0.3 , 0.6 ; 0.02 ) ( 0.4 , 0.2 ; 0.01 ) ( 0.5 , 0.6 ; 0.02 ) ( 0.6 , 0.7 ; 0.04 ) ( 0.8 , 0.4 ; 0.01 )
v e 10 ( 0.4 , 0.5 ; 0.02 ) ( 0.6 , 0.3 ; 0.01 ) ( 0.7 , 0.7 ; 0.05 ) ( 0.4 , 0.9 ; 0.09 ) ( 0.9 , 0.3 ; 0.06 )

Step 2. Create the DM using the circular 3,2-ROF data shown in Table 3.

Step 3. Here, we implement the operators:

VeA j max = C3,2-ROFWA max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) ,

VeA j min = C3,2-ROFWA min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) ,

VeG j max = C3,2-ROFWG max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) ,

and

VeG j min = C3,2-ROFWG min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) ,

for j = 1 , 2 , , 10 , with weight vectors ε = ( 0.18 , 0.21 , 0.24 , 0.20 , 0.17 ) T and putting n = 3 and m = 2 as follows in Table 4.

Table 4

Aggregated circular 3,2-ROF information matrix

Teachers/operators C3,2-ROFWA max C3,2-ROFWA min C3,2-ROFWG max C3,2-ROFWG min
v e 1 ( 0.7453 , 0.4506 ; 0.05 ) ( 0.7453 , 0.4506 ; 0.01 ) ( 0.4217 , 0.6139 ; 0.05 ) ( 0.4217 , 0.6139 ; 0.01 )
v e 2 ( 0.7150 , 0.2657 ; 0.08 ) ( 0.7150 , 0.2657 ; 0.01 ) ( 0.4438 , 0.2862 ; 0.08 ) ( 0.4438 , 0.2862 ; 0.01 )
v e 3 ( 0.7398 , 0.1599 ; 0.09 ) ( 0.7398 , 0.1599 ; 0.04 ) ( 0.5253 , 0.2343 ; 0.09 ) ( 0.5253 , 0.2343 ; 0.04 )
v e 4 ( 0.7802 , 0.2118 ; 0.09 ) ( 0.7802 , 0.2118 ; 0.05 ) ( 0.7233 , 0.2625 ; 0.09 ) ( 0.7233 , 0.2625 ; 0.05 )
v e 5 ( 0.7169 , 0.4887 ; 0.09 ) ( 0.7169 , 0.4887 ; 0.02 ) ( 0.3928 , 0.5941 ; 0.09 ) ( 0.3928 , 0.5941 ; 0.02 )
v e 6 ( 0.6970 , 0.2362 ; 0.06 ) ( 0.6970 , 0.2362 ; 0.01 ) ( 0.5445 , 0.4630 ; 0.06 ) ( 0.5445 , 0.4630 ; 0.01 )
v e 7 ( 0.7270 , 0.4229 ; 0.09 ) ( 0.7270 , 0.4229 ; 0.02 ) ( 0.4150 , 0.6417 ; 0.09 ) ( 0.4150 , 0.6417 ; 0.02 )
v e 8 ( 0.7624 , 0.2357 ; 0.09 ) ( 0.7624 , 0.2357 ; 0.03 ) ( 0.5918 , 0.3390 ; 0.09 ) ( 0.5918 , 0.3390 ; 0.03 )
v e 9 ( 0.5835 , 0.4586 ; 0.04 ) ( 0.5835 , 0.4586 ; 0.01 ) ( 0.4889 , 0.5500 ; 0.04 ) ( 0.4889 , 0.5500 ; 0.01 )
v e 10 ( 0.6904 , 0.5021 ; 0.09 ) ( 0.6904 , 0.5021 ; 0.01 ) ( 0.5718 , 0.6640 ; 0.09 ) ( 0.5718 , 0.6640 ; 0.01 )

Step 4. As shown in Table 5, we calculate the score value of VeA j max , VeA j min , VeG j max , and VeG j min for j = 1 , 2 , , 10 .

Table 5

Final score value

teachers/score value S ^ ( C3,2-ROFWA max ) S ^ ( C3,2-ROFWA min ) S ^ ( C3,2-ROFWG max ) S ^ ( C3,2-ROFWG min )
v e 1 0.2463 0.2180 0.2666 0.2949
v e 2 0.3515 0.3020 0.0620 0.0126
v e 3 0.4429 0.4075 0.1536 0.1183
v e 4 0.4936 0.4653 0.3731 0.3448
v e 5 0.1933 0.1438 0.2287 0.2782
v e 6 0.3253 0.2899 0.0105 0.0458
v e 7 0.2689 0.4088 0.2767 0.3262
v e 8 0.4513 0.0045 0.1559 0.1135
v e 9 0.0167 0.0475 0.1574 0.1786
v e 10 0.1405 0.0840 0.1903 0.2469

Step 5. The ranking of all the options utilizing Definition 4.17 according to the score values is finally displayed in Table 6. The outcomes of the options’ ranking determined by the C3,2-ROFWA max and C3,2-ROFWA min operators as follows:

Table 6

Rankings for our application

Models Ranking order Best teacher
C3,2-ROFWA max v e 4 v e 8 v e 3 v e 2 v e 6 v e 7 v e 1 v e 5 v e 10 v e 9 v e 4
C3,2-ROFWA min v e 4 v e 8 v e 3 v e 2 v e 6 v e 7 v e 1 v e 5 v e 10 v e 9 v e 4
C3,2-ROFWG max v e 4 v e 3 v e 8 v e 2 v e 6 v e 9 v e 10 v e 5 v e 1 v e 7 v e 4
C3,2-ROFWG min v e 4 v e 3 v e 8 v e 2 v e 6 v e 9 v e 10 v e 5 v e 1 v e 7 v e 4

v e 4 v e 8 v e 3 v e 2 v e 6 v e 7 v e 1 v e 5 v e 10 v e 9 ,

while the outcomes of the options’ ranking determined by the C3,2-ROFWG max and C3,2-ROFWG min operators as follows:

v e 4 v e 3 v e 8 v e 2 v e 6 v e 9 v e 10 v e 5 v e 1 v e 7 .

Consequently, v e 4 is the best option.

A chart visualization of the data from Table 5 is presented in Figure 3, providing a clear graphical comparison of the scores assigned to each teacher across different models. This figure highlights the variations in ranking and score distribution, allowing for an intuitive understanding of the performance evaluation.

Figure 3 
                  The chart visualization of data given in Table 5.
Figure 3

The chart visualization of data given in Table 5.

5.2 Application for selection a best school

To choose the best school, an MCDM problem is described in this section. The most effective tool for changing the world is education. It is a stepping stone that creates opportunities for a brighter future. A school is where the educational journey starts. It is the first institution where kids are taught all the fundamentals of education and life. Every student’s second home, a school, is a safe and supportive environment where they spend the majority of their childhood. A teacher acts as a second parent, and friend. They educate and guide pupils via their knowledge and abilities. To ensure a child’s gradual learning and growth, it is vital to select the correct school. As parents, we notice a variety of things in school because we want the best for our children. The goal of education is to create excellent human with skills and knowledge. Teachers have the ability to develop enlightened human beings. Selecting the greatest schools with the best school management system would help kids grow up to be decent people.

The following five school criteria ought to be taken into account while choosing a school for your child. The rationale behind selecting these specific criteria stems from their direct influence on the overall educational experience and outcomes for students:

  1. Academic program offerings ( C 1 ): The kind of academic programs offered by a school is one of the first deciding factors for many families. Parents who wish their children to attend college should opt for more rigorous education.

  2. Diversity ( C 2 ): Families should make it a top priority to obtain a sense of the diversity of a school. Tradeoffs regarding ethnic, economic, and other forms of diversity are common in education, regardless of the kind of school a student attends, so families should think carefully about what matters most to them. Parents should consider not only demographics but also how schools teach about racism and race, as well as how sensitively they handle different cultural concerns.

  3. Supports for special needs ( C 3 ): Larger schools may have greater resources accessible for children with special needs, which is one of their advantages. In addition, public schools are expected to give students with disabilities an appropriate education in the least restrictive setting feasible. Private educational institutions are exempt from this mandate. Even so, there are private schools that specialize in serving pupils with impairments. If a family prioritizes this, they should find out if a specific school can meet the needs of their child.

  4. Graduation and college attendance rates ( C 4 ): Even though families might not want to make their choice based only on data, some statistics might give an indication of how well a school has prepared its children for the next phase of their lives.

  5. School policies: ( C 5 ): In addition to being advantageous for children, the policies and procedures must to be clear to the parents. Establishing regulations that are not physically, mentally, or emotionally abusive will require schools to adopt a stricter code of conduct. For children’s development and learning, a school must provide a secure, hygienic, and stress-free atmosphere. It is imperative that they abstain from any form of prejudice or favoritism and oppose racism, casteism, gender inequity, and discrimination to foster unity and equality.

The aforementioned requirements are necessary, but they are not the only ones. Selecting a school is a difficult process, and the needs of the child should always come first. Every child is unique in their needs, abilities, and interests. Therefore, forcing a decision on a child that does not align with his or her wants and requirements may stunt their holistic growth and prevent them from reaching the intended learning objectives. Thus, let’s remember that safeguarding our children’s future depends greatly on the prudent choice we make today. By evaluating schools based on these criteria, we aim to provide a holistic framework that captures both the academic and social dimensions of a student’s educational journey.

To choose the best school, suppose there are four school alternatives, marked by S 1 , S 2 , S 3 , and S 4 in this MCDM problem. The associated weight vector of the attributes is ε = ( 0.22 , 0.20 , 0.21 , 0.18 , 0.19 ) T , respectively. The DM [ Ω ^ j i ] 4 × 5 is presented in Table 7, where Ω ^ j i ( j = 1 , 2 , 3 , 4 and i = 1 , 2 , 3 , 4 , 5 ) are in the form of C2,1-ROFVs. At this point, the circular 2,1-ROFV DM can be computed as follows:

Table 7

Circular 2,1-ROF values

Schools/criteria C 1 C 2 C 3 C 4 C 5
S 1 ( 0.16 , 0.83 ; 0.79 ) ( 0.67 , 0.28 ; 0.68 ) ( 0.73 , 0.21 ; 0.91 ) ( 0.79 , 0.09 ; 0.87 ) ( 0.78 , 0.11 ; 0.21 )
S 2 ( 0.71 , 0.15 ; 0.62 ) ( 0.38 , 0.61 ; 0.39 ) ( 0.61 , 0.33 ; 0.56 ) ( 0.56 , 0.22 ; 0.19 ) ( 0.41 , 0.49 ; 0.69 )
S 3 ( 0.75 , 0.23 ; 0.71 ) ( 0.91 , 0.03 ; 0.93 ) ( 0.72 , 0.19 ; 0.89 ) ( 0.79 , 0.11 ; 0.93 ) ( 0.87 , 0.26 ; 0.93 )
S 4 ( 0.54 , 0.41 ; 0.51 ) ( 0.43 , 0.55 ; 0.34 ) ( 0.71 , 0.25 ; 0.23 ) ( 0.49 , 0.41 ; 0.36 ) ( 0.70 , 0.29 ; 0.19 )

Step 2. Create the DM using the circular 2,1-ROF data shown in Table 7.

Step 3. Here we implement the operators:

VeA j max = C2,1-ROFWA max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) , VeA j min = C2,1-ROFWA min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) , VeG j max = C2,1-ROFWG max ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 )

and

VeG j min = C2,1-ROFWG min ( Ω ^ j 1 , Ω ^ j 2 , , Ω ^ j 5 ) ,

for j = 1 , 2 , 3 , 4 , with weight vectors ε = ( 0.22 , 0.20 , 0.21 , 0.18 , 0.19 ) T and putting n = 2 and m = 1 as follows in Table 8.

Table 8

Aggregated circular 2,1-ROF information matrix

Schools/operators C2,1-ROFWA max C2,1-ROFWA min C2,1-ROFWG max C2,1-ROFWG min
S 1 ( 0.6871 , 0.2285 ; 0.91 ) ( 0.6871 , 0.2285 ; 0.21 ) ( 0.5278 , 0.4197 ; 0.91 ) ( 0.5278 , 0.4197 ; 0.21 )
S 2 ( 0.5658 , 0.3144 ; 0.69 ) ( 0.5658 , 0.3144 ; 0.19 ) ( 0.5239 , 0.3817 ; 0.69 ) ( 0.5239 , 0.3817 ; 0.19 )
S 3 ( 0.8232 , 0.1318 ; 0.93 ) ( 0.8232 , 0.1318 ; 0.71 ) ( 0.8025 , 0.1698 ; 0.93 ) ( 0.8025 , 0.1698 ; 0.71 )
S 4 ( 0.5974 , 0.3670 ; 0.51 ) ( 0.5974 , 0.3670 ; 0.19 ) ( 0.5641 , 0.3912 ; 0.51 ) ( 0.5641 , 0.3912 ; 0.19 )

Step 4. As shown in Table 9, we calculate the score value of VeA j max , VeA j min , VeG j max , and VeG j min for j = 1 , 2 , 3 , 4 .

Table 9

Final score value

Schools/score value S ^ ( C2,1-ROFWA max ) S ^ ( C2,1-ROFWA min ) S ^ ( C2,1-ROFWG max ) S ^ ( C2,1-ROFWG min )
S 1 0.8871 0.3921 0.5024 0.0074
S 2 0.4937 0.1401 0.3807 0.0271
S 3 1.2035 1.0480 1.1319 0.9763
S 4 0.3505 0.1243 0.2877 0.0614

Step 5. The ranking of all the options utilizing Definition 4.17 according to the score values is finally displayed in Table 10. The outcomes of the options’ ranking determined by the C2,1-ROFWA max , C2,1-ROFWA min , and C2,1-ROFWG max operators as S 3 S 1 S 2 S 4 , while the outcomes of the options’ ranking determined by the C2,1-ROFWG min operator as S 3 S 4 S 2 S 1 . Consequently, S 3 is the best option.

Table 10

Rankings for our application

Models Ranking order Best school
C2,1-ROFWA max S 3 S 1 S 2 S 4 S 3
C2,1-ROFWA min S 3 S 1 S 2 S 4 S 3
C2,1-ROFWG max S 3 S 1 S 2 S 4 S 3
C2,1-ROFWG min S 3 S 4 S 2 S 1 S 3

A chart visualization of Table 9 is presented in Figure 4 to illustrate the comparative performance of the schools across different models. The chart provides a clear graphical representation of the data, highlighting the trends and differences in scores for the four aggregation methods.

Figure 4 
                  The chart visualization of data given in Table 9.
Figure 4

The chart visualization of data given in Table 9.

6 Comparison analysis and discussion

To demonstrate the benefits of the proposed models, we compare them to other models that are currently in use. Here, we compare our proposed models to the models by which we must compare them to confirm the precision and effectiveness of our generated model using both our data and data from a particular application.

Henceforth, this part contrasts the proposed methodology with current MCDM approaches, concentrating on the circular n,m-ROF environment for n = m = 1 , n = m = 2 , and n = m = q . As Cn,m-ROFS are generalizations of regular n,m-ROFSs, we may use ρ ^ = 0 to deduce an n,m-ROFS from any Cn,m-ROFS. A summary of the calculated results utilizing the intuitionistic fuzzy weighted average (IFWA) operator [37], IFWG operator ([38]), q-rung orthopair fuzzy weighted averaging (q-ROFWA) operator [39], and q-rung orthopair fuzzy weighted geometric (q-ROFWG) operator [39] that are currently in use can be seen in Table 11. As a result, the application in Section 5.2 is run via the operators IFWA and IFWG, which produce identical optimal outcomes. Currently available intuitionistic fuzzy sets models are not suitable for implementing the application described in Section 5.1. For varying values of q , the operators q-ROFWA and q-ROFWG are applied to the applications in Sections 5.1 and 5.2, producing the same optimal outcomes. As an outcome, the advantages of the suggested models and their comparison with alternative models may be clearly understood.

Table 11

Comparison analysis with existing models on our applications

Models: Application section Score values Option
v e 1 , v e 2 , v e 3 , v e 4 , v e 5 , v e 6 , v e 7 , v e 8 , v e 9 , v e 10
2-ROFWA : 5.1 0.3172, 0.4070, 0.4936, 0.5553, 0.2306, 0.4001, 0.3112, 0.5059, 0.1096, 0.2012 v e 4
2-ROFWG : 5.1 0.1991 , 0.1150, 0.2210, 0.4542, 0.1986 , 0.0821, 0.2396 , 0.2352, 0.0635 , 0.1139 v e 4
3-ROFWA : 5.1 0.3224, 0.3468, 0.4007, 0.4654, 0.2518, 0.3255, 0.3085, 0.4301, 0.1022, 0.2024 v e 4
3-ROFWG : 5.1 0.1925 , 0.0619, 0.1271, 0.3566, 0.1637 , 0.0172, 0.2375 , 0.1595, 0.0637 , 0.1403 v e 4
4-ROFWA : 5.1 0.2960, 0.2848, 0.3235, 0.3784, 0.2433, 0.2607, 0.2789, 0.3539, 0.0862, 0.1854 v e 4
4-ROFWG : 5.1 0.1711 , 0.0304, 0.0698, 0.2662, 0.1219 , 0.0177 , 0.2150 , 0.1017, 0.0541 , 0.1479 v e 4
5-ROFWA : 5.1 0.2602, 0.2342, 0.2639, 0.3078, 0.2223, 0.2120, 0.2433, 0.2912, 0.0693, 0.1630 v e 4
5-ROFWG : 5.1 0.1477 , 0.0143, 0.0376, 0.1952, 0.0865 , 0.0323 , 0.1877 , 0.0631, 0.0421 , 0.1438 v e 4
Proposed C5,2-ROFWA max : 5.1 0.1112, 0.2215, 0.3021, 0.3270, 0.0750, 0.1994, 0.1416, 0.3000, 0.0924 , 0.0065 v e 4
Proposed C5,2-ROFWA min : 5.1 0.0829, 0.1720, 0.2668, 0.2987, 0.0255, 0.1640, 0.0921, 0.2576, 0.1136 , 0.0501 v e 4
Proposed C5,2-ROFWG max : 5.1 0.3282 , 0.0081 , 0.0487, 0.1927, 0.2799 , 0.1241 , 0.3358 , 0.0213, 0.2463 , 0.3161 v e 4
Proposed C5,2-ROFWG min : 5.1 0.3565 , 0.0576 , 0.0134, 0.1644, 0.3294 , 0.1594 , 0.3853 , 0.0212 , 0.2675 , 0.3727 v e 4
S 1 , S 2 , S 2 , S 4
IFWA or 1-ROFWA : 5.2 0.4398, 0.2424, 0.6896, 0.2237 S 3
IFWG or 1-ROFWG : 5.2 0.1082, 0.1422, 0.6327, 0.1730 S 3
2-ROFWA : 5.2 0.4199, 0.2213, 0.6603, 0.2222 S 3
2-ROFWG : 5.2 0.0295, 0.1069, 0.6093, 0.1569 S 3
3-ROFWA : 5.2 0.3313, 0.1593, 0.5596, 0.1715 S 3
3-ROFWG : 5.2 0.0288 , 0.0617, 0.5091, 0.1092 S 3
4-ROFWA : 5.2 0.2505, 0.1069, 0.4678, 0.1220 S 3
4-ROFWG : 5.2 0.0559 , 0.0322, 0.4130, 0.0690 S 3
5-ROFWA : 5.2 0.1878, 0.0704, 0.3920, 0.0842 S 3
5-ROFWG : 5.2 0.0637 , 0.0157, 0.3324, 0.0417 S 3
Proposed C4,1-ROFWA max : 5.2 0.6681, 0.2902, 0.9940, 0.1338 S 3
Proposed C4,1-ROFWA min : 5.2 0.1732, 0.0633 , 0.8384, 0.0925 S 3
Proposed C4,1-ROFWG max : 5.2 0.3014, 0.1815, 0.9026, 0.0707 S 3
Proposed C4,1-ROFWG min : 5.2 0.1936 , 0.1720 , 0.7470, 0.1555 S 3

In addition, I have used the techniques described here on the application given in ([34], Table 2) to contrast the C n , m -ROFWA max , C n , m -ROFWA min , C n , m -ROFWG max , and C n , m -ROFWAG min operators with the CPFWA max and CPFWA min operators for various parameters n and m . Table 12 demonstrates that the best choice of object for the proposed C n , m -ROFWA max , C n , m -ROFWA min , C n , m -ROFWG max , and C n , m -ROFWAG min operators with the CPFWA max and CPFWA min operators remains the same for different values of n and m . For this reason, we propose the theory of Cn,m-ROFSs, which has more applications than the existing circular fuzzy set models, making it better than CPFS. This implies that our method is dependable and applicable to problems involving decision-making. Since it is the more adaptable, sophisticated, and comprehensive model, the suggested approaches address more MCDM problems by using different values of the parameters n and m that are acceptable for MCDM problems. We consequently support the theory of circular n,m-ROFVs, which is more widely applicable than the existing fuzzy set and circular fuzzy set models, and hence better than them.

Table 12

Applying the suggested operators to the application in [34] and compared results

Models S ^ ( 1 ) S ^ ( 2 ) S ^ ( 3 ) S ^ ( 4 ) Ranking Best option
Proposed C2,3-ROFWA max 0.4732 0.4750 0.5496 0.5353 3 4 2 1 3
Proposed C2,3-ROFWA min 0.4449 0.4538 0.5143 0.4999 3 4 2 1 3
Proposed C4,2-ROFWA max 0.1790 0.1827 0.2688 0.2455 3 4 2 1 3
Proposed C4,2-ROFWA min 0.1508 0.1615 0.2335 0.2101 3 4 2 1 3
Proposed C12,2-ROFWA max 0.0184 0.0079 0.0389 0.0117 3 4 2 1 3
Proposed C12,2-ROFWA min 0.0466 0.0291 0.0036 0.0237 3 4 2 1 3
Proposed C3,4-ROFWG max 0.3267 0.3128 0.3866 0.3730 3 4 1 2 3
Proposed C3,4-ROFWG min 0.2985 0.2916 0.3513 0.3376 3 4 1 2 3
Proposed C5,3-ROFWG max 0.1417 0.1334 0.2001 0.1802 3 4 1 2 3
Proposed C5,3-ROFWG min 0.1134 0.1122 0.1647 0.1449 3 4 1 2 3
CPFWA max [34] 0.0150 0.0040 0.0163 0.0030 3 4 2 1 3
CPFWA min [34] 0.0150 0.0040 0.0163 0.0030 3 4 2 1 3

7 Sensitivity analysis and limitations of the proposed models

7.1 Sensitivity analysis of the proposed models

The sensitivity of the proposed decision support model was evaluated by analyzing the impact of varying the parameters n and m on the ranking results. The results demonstrate the influence of parameter values on the performance of the Cn,m-ROFWA and Cn,m-ROFWG operators under different scenarios:

  1. Large values for both parameters ( n and m ): When both parameters n and m are set to large values, the score values generated by the proposed operators tend to converge, assigning nearly identical values to the alternatives. This convergence reduces the ability of the operators to distinguish between alternatives, leading to less reliable rankings. The results for this scenario are presented in Table 13, which highlights the limited variation in score values across alternatives under large parameter settings. Moreover, a chart visualization of Table 13 is given in Figure 5.

  2. Small values for both parameters ( n and m ): With smaller values of n and m , the proposed operators achieve optimal performance, effectively differentiating among alternatives. In this scenario, the score values distinctly identify the best option, providing a robust and reliable ranking. These results are shown in Tables 5, 9, 11, and 12 demonstrating the model’s effectiveness in practical decision-making with small parameter values.

  3. Combination of large and small values for n and m : When one parameter is set to a large value while the other remains small, the operators still maintain their ability to rank alternatives effectively, producing reliable outcomes. This scenario reflects the flexibility of the proposed model to adapt to mixed parameter settings while retaining accuracy in the selection of the best alternative.

Table 13

Sensitivity analysis for Example 5.1 concerning parameters n and m

Teachers S ^ ( C998,999-ROFWA max ) S ^ ( C998,999-ROFWG max ) Ranking order
v e 1 0.0354 0.0354
v e 2 0.0566 0.0566
v e 3 0.0636 0.0636
v e 4 0.0636 0.0636
v e 5 0.0636 0.0636 v e 3 v e 4 v e 5 v e 7 v e 8 v e 10 v e 2 v e 6 v e 1 v e 9
v e 6 0.0424 0.0424
v e 7 0.0636 0.0636
v e 8 0.0636 0.0636
v e 9 0.0283 0.0283
v e 10 0.0636 0.0636
Figure 5 
                  The chart visualization of data given in Table 13.
Figure 5

The chart visualization of data given in Table 13.

7.2 Limitations of the proposed models

  1. Impact of large parameter values: When the parameters n and m take large values, the ranking order of the proposed models is affected. In such cases, the score values generated by the Cn,m-ROFWA and n,m-ROFWG operators tend to converge, assigning similar values to the alternatives. It is shown in the sensitivity analysis (Table 13). This limits the models’ ability to differentiate effectively between options.

  2. Preference for small parameter values: To achieve better results and maintain distinction in ranking, it is recommended to use small values for n and m . This ensures the operators can adequately capture the variation among alternatives.

  3. Limited practical applicability of large parameter values: Real-world scenarios involving large values for n and m are uncommon. As a result, such cases can often be avoided, focusing instead on more practical and frequently occurring situations with smaller parameter values.

8 Conclusions

This study introduced the concept of Cn,m-ROFS, which extends the capabilities of existing fuzzy sets like IFS, PFS, FFS, and q-ROFS by accommodating a larger range of fuzzy data. By employing a circular model with a radius ρ and a pair at its center, this framework enables more flexible and accurate representation of fuzzy information, thus enhancing decision-making. The Cn,m-ROFS is a generalization of circular IFSs, circular PFSs, and circular q-ROFSs, allowing for broader evaluation areas for decision-makers. In addition, algebraic operations and weighted aggregation methods for Cn,m-ROFS are defined, and the approach is demonstrated through an MCDM case study for teacher and school selection. A comparison with existing methods confirms the robustness and flexibility of the proposed model.

Future research can focus on exploring additional aggregation operators, expanding the range of applications for Cn,m-ROFSs, and incorporating distance and similarity measures. Further studies could investigate complex, interval-valued, and bipolar Cn,m-ROFSs to enhance the model’s applicability. Moreover, integrating deep learning techniques with decision models could be an exciting direction for addressing more complex and high-stakes decision-making scenarios.

  1. Author contributions: HZI: writing-original draft, conceptualization, methodology, supervision, software, reviewing and investigation. IA: reviewing and investigation.

  2. Conflict of interest: The authors declare that they have no conflict of interest regarding the publication of the research article.

  3. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

  4. Data availability statement: No data were used to support this study.

References

[1] L. A. Zadeh, Fuzzy sets, Inf. Control 8 (1965), 338–353. 10.1016/S0019-9958(65)90241-XSearch in Google Scholar

[2] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20 (1986), 87–96. 10.1016/S0165-0114(86)80034-3Search in Google Scholar

[3] R. R. Yager, Pythagorean fuzzy subsets, 2013 Joint IFSA Wworld Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, Edmonton, AB, Canada, 2013, pp. 57–61. 10.1109/IFSA-NAFIPS.2013.6608375Search in Google Scholar

[4] R. R. Yager, Generalized orthopair fuzzy sets, IEEE Trans. Fuzzy Syst. 25 (2016), no. 5, 1222–1230. 10.1109/TFUZZ.2016.2604005Search in Google Scholar

[5] T. Senapati and R. R. Yager, Fermatean fuzzy sets, J. Ambient Intell. Humaniz. Comput. 11 (2020), 663–674. 10.1007/s12652-019-01377-0Search in Google Scholar

[6] H. Z. Ibrahim and I. Alshammari, n,m-rung orthopair fuzzy sets with applications to multicriteria decision-making, IEEE Access 10 (2022), 99562–99572. 10.1109/ACCESS.2022.3207184Search in Google Scholar

[7] S. Bekesiene and S. Mashchenko, On nash equilibria in a finite game for fuzzy sets of strategies, Mathematics 11 (2023), 4619, DOI: https://doi.org/10.3390/math11224619. 10.3390/math11224619Search in Google Scholar

[8] F. Chacón-Gómez, M. E. Cornejo, and J. Medina, Decision making in fuzzy rough set theory, Mathematics 11 (2023), 4187, DOI: https://doi.org/10.3390/math11194187. 10.3390/math11194187Search in Google Scholar

[9] Z. Liu, Credal-based fuzzy number data clustering, Granul. Comput. 8 (2023), 1907–1924, DOI: https://doi.org/10.1007/s41066-023-00410-0. 10.1007/s41066-023-00410-0Search in Google Scholar

[10] A. Hussain and K. Ullah, An intelligent decision support system for spherical fuzzy Sugeno-Weber aggregation operators and real-life applications, Spec. Mech. Eng. Oper. Res. 1 (2024), no. 1, 177–188, DOI: https://doi.org/10.31181/smeor11202415. 10.31181/smeor11202415Search in Google Scholar

[11] J. Kannan, V. Jayakumar, and M. Pethaperumal, Advanced fuzzy-based decision-making: The linear diophantine fuzzy CODAS method for logistic specialist selection, Spec. Oper. Res. 2 (2024), no.1, 41–60, DOI: https://doi.org/10.31181/sor2120259. 10.31181/sor2120259Search in Google Scholar

[12] C. Kahraman, S. Cebi, B. Oztaysi, and S. C. Onar, Intuitionistic fuzzy sets with ordered pairs and their usage in multi-attribute decisionm making: A novel intuitionistic fuzzy TOPSIS method with ordered pairs, Mathematics 11 (2023), 3867, DOI: https://doi.org/10.3390/math11183867. 10.3390/math11183867Search in Google Scholar

[13] X. Yang, T. Mahmood, Z. Ali, and K. Hayat, Identification and classification of multi-attribute decision-making based on complex intuitionistic fuzzy frank aggregation operators, Mathematics 11 (2023), 3292, DOI: https://doi.org/10.3390/math11153292. 10.3390/math11153292Search in Google Scholar

[14] C. Dhankhar and K. Kumar, Multi-attribute decision-making based on the advanced possibility degree measure of intuitionistic fuzzy numbers, Granul. Comput. 8 (2023), 467–478. 10.1007/s41066-022-00343-0Search in Google Scholar

[15] P. Liu, S. M. Chen, and Y. Wang, Multiattribute group decision-making based on intuitionistic fuzzy partitioned maclaurin symmetric mean operators, Inform. Sci. 512 (2020), 830–854. 10.1016/j.ins.2019.10.013Search in Google Scholar

[16] X. Y. Zou, S. M. Chen, and K. Y. Fan, Multiple attribute decision-making using improved intuitionistic fuzzy weighted geometric operators of intuitionistic fuzzy values, Inform. Sci. 535 (2020), 242–253. 10.1016/j.ins.2020.05.011Search in Google Scholar

[17] F. Meng, S. M. Chen, and R. Yuan, Group decision-making with heterogeneous intuitionistic fuzzy preference relations, Inform. Sci. 523 (2020), 197–219. 10.1016/j.ins.2020.03.010Search in Google Scholar

[18] Z. Zhang, S. M. Chen, and C. Wang, Group decision-making with incomplete intuitionistic multiplicative preference relations, Inform. Sci. 516 (2020), 560–571. 10.1016/j.ins.2019.12.042Search in Google Scholar

[19] R. Imran, K. Ullah, Z. Ali, and M. Akram, A multi-criteria group decision-making approach for robot selection using interval-valued intuitionistic fuzzy information and aczel-alsina bonferroni means, Spec. Decis. Mak. Appl. 1 (2024), no. 1, 1–32, DOI: https://doi.org/10.31181/sdmap1120241. 10.31181/sdmap1120241Search in Google Scholar

[20] G. Shahzadi, M. Akram, and A. N. Al-Kenani, Decision-making approach under pythagorean fuzzy Yager weighted operators, Mathematics 8 (2020), 70, DOI: https://doi.org/10.3390/math8010070. 10.3390/math8010070Search in Google Scholar

[21] Q. Zhou, H. Mo, and Y. Deng, A new divergence measure of pythagorean fuzzy sets based on belief function and its application in medical diagnosis, Mathematics 8 (2020), 142, DOI: https://doi.org/10.3390/math8010142. 10.3390/math8010142Search in Google Scholar

[22] M. Asif, U. Ishtiaq, and I. K. Argyros, Hamacher aggregation operators for pythagorean fuzzy set and its application in multi-attribute decision-making Problem, Spec. Oper. Res. 2 (2024), no. 1, 27–40, DOI: https://doi.org/10.31181/sor2120258. 10.31181/sor2120258Search in Google Scholar

[23] I. U. Haq, T. Shaheen, W. Ali, H. Toor, T. Senapati, F. Pilla, et al., Novel Fermatean fuzzy Aczel-Alsina model for investment strategy selection, Mathematics 11 (2023), 3211, DOI: https://doi.org/10.3390/math11143211. 10.3390/math11143211Search in Google Scholar

[24] T. Senapati, and R.R. Yager, Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision-making, Informatica 30 (2019), no. 2, 391–412. 10.15388/Informatica.2019.211Search in Google Scholar

[25] T. Senapati and R.R. Yager, Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods, Eng. Appl. Artif. Intell. 85 (2019), 112–121. 10.1016/j.engappai.2019.05.012Search in Google Scholar

[26] A. Mishra and P. Rani, A q-rung orthopair fuzzy aras method based on entropy and discrimination measures: An application of sustainable recycling partner selection, J. Ambient Intell. Humaniz. Comput. 14 (2023), 6897–6918. 10.1007/s12652-021-03549-3Search in Google Scholar PubMed PubMed Central

[27] H. Z. Ibrahim, Topological approaches for n, m-rung orthopair fuzzy sets with applications to diagnosis of learning disability, J. Intel. Fuzzy Syst. 44 (2023), no. 1, 855–869. 10.3233/JIFS-221528Search in Google Scholar

[28] K. T. Atanassov, Circular intuitionistic fuzzy sets, J. Intell. Fuzzy Syst. 39 (2020), no. 5, 5981–5986, DOI: https://doi.org/10.3233/JIFS-189072. 10.3233/JIFS-189072Search in Google Scholar

[29] K. T. Atanassov and E. Marinov, Four distances for circular intuitionistic fuzzy sets, Mathematics 9 (2021), 1121, DOI: https://doi.org/10.3390/math9101121. 10.3390/math9101121Search in Google Scholar

[30] C. Kahraman and N. Alkan, Circular intuitionistic fuzzy TOPSIS method with vague membership functions: supplier selection application context, Notes Intuitionistic Fuzzy Sets 27 (2021), no. 1, 24–52. 10.7546/nifs.2021.27.1.24-52Search in Google Scholar

[31] T. Y. Chen, Evolved distance measures for circular intuitionistic fuzzy sets and theirexploitation in the technique for order preference by similarity to ideal solutions, Artif. Intell. Rev. 56 (2023), 7347–7401, DOI: https://doi.org/10.1007/s10462-022-10318-x. 10.1007/s10462-022-10318-xSearch in Google Scholar PubMed PubMed Central

[32] C. Kahraman and I. Otay, Extension of VIKOR method using circular intuitionistic fuzzy sets, In: C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, I. U. Sari (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021, Lecture Notes in Networks and Systems, vol. 308, Springer, Cham, 2022. 10.1007/978-3-030-85577-2Search in Google Scholar

[33] E. Boltürk and C. Kahraman, Interval-valued and circular intuitionistic fuzzy present worth analyses, Informatica 33 (2022), no. 4, 693–711, DOI: https://doi:10.15388/22-INFOR478. 10.15388/22-INFOR478Search in Google Scholar

[34] M. J. Khan, J. C. R. Alcantud, W. Kumam, P. Kumam, and N. A. Alreshdi, Expanding pythagorean fuzzy sets with distinctive radii: disc pythagorean fuzzy sets, Complex Intell. Syst. 9 (2023), 7037–7054, DOI: https://doi.org/10.1007/s40747-023-01062-y. 10.1007/s40747-023-01062-ySearch in Google Scholar

[35] B. Yusoff, A. Kilicman, D. Pratama, and R. Hasni, Circular q-rung orthopair fuzzy set and its algebraic properties, Malays. J. Math. Sci. 17 (2023), 363–378. 10.47836/mjms.17.3.08Search in Google Scholar

[36] Z. Ali and M.-S. Yang, On circular q-rung orthopair fuzzy sets with dombi aggregation operators and application to symmetry analysis in artificial intelligence, Symmetry 16 (2024), 260, DOI: https://doi.org/10.3390/sym16030260. 10.3390/sym16030260Search in Google Scholar

[37] Z. Xu, Intuitionistic fuzzy aggregation operators, IEEE Trans. Fuzzy Syst. 15 (2007), no. 6, 1179–1187, DOI: https://doi.org/10.1109/TFUZZ.2006.890678. 10.1109/TFUZZ.2006.890678Search in Google Scholar

[38] Z. Xu and R. R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, Int. J. Gen. Syst. 35 (2006), no. 4, 417–433, DOI: https://doi.org/10.1080/03081070600574353. 10.1080/03081070600574353Search in Google Scholar

[39] P. Liu and P. Wang, Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision-making, Int.J. Intell. Sys. 33 (2018), no. 2, 259–280, DOI: https://doi.org/10.1002/int.21927. 10.1002/int.21927Search in Google Scholar

Received: 2024-04-20
Revised: 2024-11-25
Accepted: 2025-01-03
Published Online: 2025-02-11

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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