Research Article | Open Access

# Hesitant Fuzzy Linguistic Multicriteria Decision-Making Method Based on Generalized Prioritized Aggregation Operator

**Academic Editor:**Luis Martínez

#### Abstract

Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

#### 1. Introduction

Since the fuzzy set was proposed by Zadeh in 1965 [1], it has been widely researched and developed as well as being successfully applied in various fields [2–5]. Due to the fuzziness and uncertainty of many MCDM problems, the criteria weights and values of alternatives can be inaccurate, uncertain, or incomplete. Under such circumstances, Zadeh’s fuzzy sets can provide robust solutions. In Zadeh’s fuzzy sets, the membership degree of the element in a universe is a single value between zero and one; however, these single values are inadequate to provide complete information due to a lack of systematic and comprehensive knowledge.

Hesitant fuzzy sets (HFSs), an extension of traditional fuzzy sets, can address this problem. HFSs were first introduced by Torra and Narukawa [6, 7], and they permit the membership degree of an element to be a set of several possible values between zero and one. HFSs are highly useful in expressing hesitance existing when decision makers give the evaluation values, and they have been a subject of great interest to researchers. For example, some work on the aggregation operators of HFSs has been undertaken in [8–11], and the distance and correlation measures for HFSs were developed in [12–15]. Later still, hesitant fuzzy TOPSIS [16] and hesitant fuzzy TODIM [17] methods for solving MCDM problems have been proposed.

When faced with problems that are too complex or ill-defined to be solved by quantitative expressions, linguistic variables can be an effective tool because the use of linguistic information enhances the reliability and flexibility of classical decision models [18]. Linguistic variables have been studied in depth and used in many fields [19–23]. The linguistic variable could be a single linguistic term [24], or interval of linguistic terms, that is, uncertain linguistic variables [25]. Rodríguez et al. [26, 27] proposed hesitant fuzzy linguistic term sets (HFLTSs) that assess a linguistic variable by using several linguistic terms. However, similar to linguistic variables, they cannot reflect the possible membership degrees of a linguistic term to a given concept. The information they express is not sufficiently comprehensive and they cannot deal with problems in which both the evaluation value and its associate membership degrees are described through fuzzy concepts. By contrast, intuitionistic linguistic sets (ILSs) [28] and their extensions [29, 30] can describe two fuzzy attributes of an object: a linguistic variable and an intuitionistic fuzzy number. The former provides an evaluation value, whilst the latter describes the confidence degree for the given evaluation value.

To express decision-makers’ hesitance that exists in giving the associated membership degrees of one linguistic term, the concept of hesitant fuzzy linguistic sets (HFLSs), which is based on linguistic term sets and HFSs, was introduced in [31]. The elements in HFLSs are called hesitant fuzzy linguistic numbers (HFLNs). That is to say, for one object, an HFLS is reduced to an HFLN, which can be considered as a special case of HFLSs. For example, is an HFLN and 0.3, 0.4, and 0.5 are the possible membership degrees to the linguistic term . HFLSs have enabled great progress in describing linguistic information and to some extent may be considered an innovative construct. The main advantage of HFLSs is that they can describe two fuzzy attributes of an object: a linguistic term and a hesitant fuzzy element (HFE). The former provides an evaluation value, such as “excellent” or “good.” The latter describes the hesitancy for the given evaluation value and denotes the membership degrees associated with the specific linguistic term. However, the operations proposed by Lin et al. [31] have some limitations that will be discussed in Section 3, and this paper will define new operations for HFLNs.

To date, several methods have been proposed for dealing with linguistic information and the main ones will now be briefly described. One of the methods is based on a transformation to fuzzy numbers, which converts linguistic information into triangular, or other kinds of fuzzy numbers by means of a membership function [32–34]. However, this method led to a certain degree of information loss in the transformation process and it is difficult to choose the appropriate membership functions in practical decision-making applications. Another method is based on symbols that made computations on the subscripts of linguistic terms and was easy to operate [35–38]. However, in order to express the results in the initial term sets, this method performed the retranslation step as an approximation process, which led to a lack of accuracy [39]. One method is based on the cloud model, which can correctly depict the uncertainty of a qualitative concept. This model has been successfully utilized [40–42]. Another method is based on the 2-tuple linguistic representation model [43], which avoided the information distortion and loss that had hitherto occurred in linguistic information processing [44–47]. In this method, there is a conversion and inverse conversion process. Motivated by this idea and taking into consideration the limitations in previous linguistic methods, Wang et al. [48] proposed linguistic scale functions to deal with linguistic translation issues under different semantic situations. These scale functions provide a higher degree of flexibility for modeling linguistic information.

In general, aggregation operators are important tools for dealing with information fusion in MCDM problems and are a research area of great interest throughout the world. In practical situations, decision makers usually consider different criteria priorities. To deal with this issue, Yager [49] developed prioritized average (PA) operators by modeling the criteria priority on the weights associated with criteria, which are dependent on the satisfaction of higher priority criteria. Yager [50] further focused on PA operators and proposed two methods for formulating this type of aggregation process. As is well known, the PA operator has many advantages over other operators. For example, the PA operator does not need to provide weight vectors and, when using this operator, it is only necessary to know the priority among criteria. However, Yager [49] only discussed the criteria values and weights in real number domain, and there has been no aggregation operator that considers different criteria priorities in the aggregation process for HFLNs. Therefore, the aim of this paper is to develop some PA operators for aggregating hesitant fuzzy linguistic information.

The paper will focus on a type of MCDM problems where criteria priority exists, referred to as a prioritized MCDM problem. Two PA operators and one generalized PA operator for HFLSs will be proposed under a hesitant fuzzy linguistic environment. These operators are mainly used for solving hesitant fuzzy linguistic MCDM problems in which the criteria are in different priority levels. Therefore, the rest of this paper is organized as follows. In Section 2, some basic concepts of linguistic term sets and HFSs are briefly reviewed. In Section 3, new operations of HFLNs are provided and a method for comparing two HFLNs is proposed based on the linguistic scale functions. In Section 4, the PA operators for HFLNs are proposed and some desirable properties are analyzed. Then, a method for solving MCDM problems with HFLNs, in which the criteria are in different priority levels, is developed. In Section 5, an illustrative example is provided and subsequently the comparison analysis is made. Finally, the conclusions are drawn in Section 6.

#### 2. Preliminaries

Before discussing HFLSs, some related concepts, such as linguistic term sets and HFSs are reviewed in this section. These concepts can lead to a better understanding of HFLSs.

##### 2.1. The Linguistic Term Sets and Their Extension

Let be a finite and linguistic term set with odd cardinality, where represents a possible value for a linguistic variable and should satisfy the following characteristics [32].(1)The set is ordered: , if .(2)There is a negation operator: satisfying .

For example, when , a linguistic term set could be given as follows:

When aggregating information as part of the decision-making process, the aggregated results do not regularly match the elements in the language assessment scale. To preserve all the information provided, Xu [51, 52] extended the discrete linguistic term set to a continuous linguistic term set , in which if , and is a sufficiently large positive integer. If , then is called the original linguistic term; otherwise, is called the virtual linguistic term.

In general, decision makers use original linguistic terms to evaluate alternatives, whereas virtual linguistic terms are only used as part of the calculation process in order to avoid information loss and generally enhance the overall decision making [51]. Virtual linguistic terms have no practical meaning, with their main role being to rank the alternatives [53].

##### 2.2. HFSs

*Definition 1 (see [6]). *Let be a reference set, and let a hesitant fuzzy set (HFS) on be in terms of a function that will return a subset of in the case of it being applied to .

To be easily understood, Xia and Xu [54] expressed HFSs by a mathematical symbol: where is a set of values in , denoting the possible membership degrees of the element to the set . is called a hesitant fuzzy element (HFE) [54].

*Example 2. *Let be a universal set, and two HFEs and , respectively, denote the membership degrees of to the set . is an HFS, where .

*Definition 3 (see [54]). *For an HFE , let be the number of values in , and then is called the score function of . For two HFEs and , if , then is superior to , denoted by ; if , then is indifferent to , denoted by .

#### 3. HFLNs and Their Operations

HFLNs, as the elements and special case of HFLSs, have great significance for information evaluation. In this section, the advantages and applications of HFLNs are firstly introduced. Then, new operations and comparison laws of HFLNs, which will be used in the latter analysis, are also presented.

##### 3.1. HFLSs

*Definition 4 (see [31]). *Let be a fixed set and . An HFLS in is an object:
where is a set of finite numbers in and denotes the possible membership degrees that belongs to .

When has only one element, the HFLS is reduced to . For computational convenience, we call as an HFLN.

When has only one element, it indicates that the degree that belongs to is . For example, is called a fuzzy linguistic number, which is a special case of HFLN.

*Example 5. *Let be a universal set. If an HFLS is divided into two subsets that contain only one object, respectively, then and are HFLNs. , , and are the possible membership degrees that belongs to ; , , and are the possible membership degrees that belongs to .

An HFLN is an extension of a linguistic term and an HFE. Compared to linguistic terms, HFLNs embody the possible membership degrees that an evaluation object attaches to the linguistic term, and they can depict the fuzziness more accurately than an uncertain linguistic variable does. When compared to HFEs, HFLNs add linguistic terms and assign the membership function to a specific linguistic evaluation value, which make the membership degrees no longer relative to a fuzzy concept, but to linguistic terms, such as “poor” or “good.”

In fuzzy set theory, the hesitant values in HFLNs are called possible membership degrees, which are caused by the hesitancy and uncertainty of decision makers. In the example of the performance evaluation of a car, suppose that “good ()” is an acceptable evaluation result for the car and is given by three decision makers. Then, each decision maker uses a value to express his/her opinion about the car under the evaluation of “good ().” Decision maker A may give the value for “good,” whilst decision maker B may give and decision maker C may give . In this case, HFLNs may be a better choice, and the evaluation result can be denoted by .

HFLSs and linguistic hesitant fuzzy sets (LHFSs) [55] are different concepts. An HFLS is defined on a finite set (a set of objects) , while an LHFS is defined for one object . For example, is an HFLS, and is an LHFS. In the HFLS , , and are evaluation values for objects , , and , respectively. In the LHFS , , , and are considered as a whole, which are the evaluation values for . To some extent, an LHFS can be considered to be composed of several HFLNs. The elements in , that is, , , and , are regarded as three HFLNs. Such a processing may distort the initial definitions of HFLNs and LHFSs but can make some useful operations and algorithm of HFLNs be feasible for LHFSs. In summary, LHFSs are more complex for experts or decision makers to express their preference than HFLNs because both the linguistic terms and their membership degrees in LHFSs are uncertain and inconsistent simultaneously.

##### 3.2. Linguistic Scale Functions

To use data more efficiently and to express the semantics more flexibly, linguistic scale functions assign different semantic values to linguistic terms under different situations [48]. They are preferable in practice because these functions are flexible and can give more deterministic results according to different semantics.

*Definition 6 (see [48]). *If is a numeric value, then the linguistic scale function that conducts the mapping from to is defined as follows:
where .

Clearly, the symbol reflects the preference of the decision makers when they are using the linguistic term . Therefore, the function/value in fact denotes the semantics of the linguistic terms. Consider

The evaluation scale of the linguistic information given above is divided on average. Consider

With the extension from the middle of the given linguistic term set to both ends, the absolute deviation between adjacent linguistic subscripts also increases. Consider

With the extension from the middle of the given linguistic term set to both ends, the absolute deviation between adjacent linguistic subscripts will decrease.

To preserve all the given information and facilitate the calculation, the above function can be expanded to , which satisfies , and is a strictly monotonically increasing and continuous function. Therefore, the mapping from to is one-to-one because of its monotonicity, and the inverse function of exists and is denoted by .

##### 3.3. Operations of HFLNs

*Definition 7 (see [31]). *Let and be two HFLNs. Some operations of and are defined as follows:(1);(2);(3);(4).

The operations proposed in [31] have some obvious limitations. (a) All operations are carried out directly based on the subscripts of linguistic terms, which cannot reveal the critical differences of final results under various semantic situations. (b) The two parts of HFLNs are processed separately in the additive operation, that is, of Definition 7, which may ignore the correlation of them. Take ; for example, 0.3 and 0.4 are the possible membership degrees that the object belongs to ; that is, is the explanatory part of and should be closely related to in the additive operation.

In order to overcome the existing limitations given above, new operations of HFLNs based on linguistic scale functions are defined as follows.

*Definition 8. *Let and be two HFLNs. Some operations of and are defined as follows:(1)(2)(3)(4)(5)

According to Definition 6, it is known that is a mapping from the linguistic term to the numeric value and is a mapping from to . So, the first part of is a linguistic term. In addition, it is obvious that the second part of is an HFE. In summary, according to Definition 4, it is known that the results obtained by Definition 8 are also HFLNs.

The operations defined above are based on linguistic scale functions, which can get different results when a different linguistic scale function is utilized. Thus, decision makers can flexibly select the linguistic scale function depending on their preferences and the actual semantic situations. In addition, the new addition operation of HFLNs is more reasonable and reliable, because the final hesitant fuzzy membership has closely combined each element of the original HFLNs.

, , , and necessarily appear in defining basic operations, but their results have no practical meaning. In the aggregation process, for example, using weighted operators, is combined with and is combined with ; therefore, the calculation results are interpretable in practice.

*Example 9. *Let = {very poor, poor, slightly poor, fair, slightly good, good, very good}, , , and .

If , then(1);(2);(3);(4);(5).If ,
then(1);(2);(3);(4);(5).If ,
then(1);(2);(3);(4);(5).

It can be easily proven that all the results given above are also HFLNs. In terms of the corresponding operations of HFLNs, the following theorem can also be proven easily.

Theorem 10. *Let be any three HFLNs; thus the following properties are true.*(1)*;*(2)*;*(3)*;*(4)*;*(5)*, ;*(6)*, .*

*Proof. *According to Definition 8, it is known that Properties , , and are obvious, so the proof of Property is provided now. Consider

So, .

Similarity, Properties and can be easily proven.

##### 3.4. Comparison Method for HFLNs

*Definition 11. *Let be an HFLN. The score function of can be represented as follows:
where is the score function of .

*Example 12. *Let . If and , by applying (16), then

*Definition 13. *Let be an HFLN. A variance function of can be denoted by . So, the accuracy function of can be represented as follows:
where is the number of the values in .

*Example 14. *Let . If and , by applying (18), then

*Definition 15. *Let and be any two HFLNs.(1)If , then .(2)If , then if , then ; if , then .

*Example 16. *Let and . If and , then , , , and thus .

#### 4. Hesitant Fuzzy Linguistic Prioritized Aggregation Operations and Their Applications in MCDM Problems

In this section, two prioritized aggregation operators for HFLNs are proposed based on the PA operator, and some desirable properties are also analyzed. Subsequently, these operators are extended to a generalized form. Finally, a method for solving MCDM problems with HFLNs, where the criteria are in different priority levels, is developed.

The PA operator was originally introduced by Yager [49] and is shown as follows.

*Definition 17 (see [49]). *Let be a collection of criteria and ensure that there is a prioritization between the criteria expressed by the linear ordering , which indicates that the criteria has a higher priority than , if . is an evaluation value denoting the performance of the alternative under the criteria and satisfies . If
where , and , then PA is called the PA operator.

PA operators have usually been used in situations where input arguments are exact values. Therefore, PA operators could be extended to accommodate situations where the input arguments are hesitant fuzzy linguistic information. Based on Definition 17, assume and are two sets of criteria values under criteria , where . Now the PA operators under a hesitant fuzzy linguistic environment will be analyzed in the following subsections.

##### 4.1. The Hesitant Fuzzy Linguistic Prioritized Weighted Average (HFLPWA) Operator

In this subsection, the prioritized weighted average operator under a hesitant fuzzy linguistic environment is investigated. The definition of the HFLPWA operator and its relevant theorems are given as follows.

*Definition 18. *Let be a collection of HFLNs, and then the HFLPWA operator can be defined as follows:
where , , and is the score function of .

Based on the operations of HFLNs described in Section 3, Theorem 19 can be deduced. The HFLPWA operator defined in Definition 18 is an abstract expression, whereas Theorem 19 gives the specific expression for it.

Theorem 19. *Let be a collection of HFLNs. Then the aggregated value, obtained by using the HFLPWA operator, is also an HFLN, and
**
where , , and is the score function of .*

*Proof. *Clearly, according to Definition 8, the aggregated value is also an HFLN. In the following, (22) is proven by using a mathematical induction on .(1)For , since
we have
(2)If (22) holds for , then
When , by the operations described in Section 3, we have
that is, (22) holds for . Thus, (22) holds for all . Now

Theorem 20 (boundedness). *Let be a collection of HFLNs. If , , where and , then
*

*Proof. *Let , and then .

Since for all , we have
and then
Similarly, since
therefore
So

Theorem 21 (commutativity). *Let be a collection of HFLNs and be any permutation of . Then
*

The weight of is decided by the priority and value of and will not be influenced by its position in the permutation. So, Theorem 21 can be easily proven, and the proof is therefore omitted.

It should be noted that the HFLPWA operator cannot satisfy idempotency. Take , for example, and if , then . In addition, we do not consider the monotonicity of HFLPWA because the weights will be recalculated and vary if the values used in the HFLPWA operator change. It is difficult to consider the monotonic property when the parameters are irregularly variable.

##### 4.2. The Hesitant Fuzzy Linguistic Prioritized Weighted Geometric (HFLPWG) Operator

In this subsection, the prioritized weighted geometric operator under a hesitant fuzzy linguistic environment is investigated. The definition of the HFLPWG operator and its relevant theorems are given as follows.

*Definition 22. *Let be a collection of HFLNs, and then the HFLPWG operator can be defined as follows:
where , , and is the score function of .

Similar to the HFLPWA operator, the HFLPWG operator satisfies the following properties.

Theorem 23. *Let be a collection of HFLNs. Then the aggregated value, obtained by using the HFLPWG operator, is also an HFLN, and
**
where , , and is the score function of .*

*Proof. *Clearly, according to Definition 8, the aggregated value is also an HFLN. In the following, (36) is proven by using a mathematical induction on .(1)For , since
we have
(2)If (36) holds for , then
When , by the operations described in Section 3, we have
that is, (36) holds for . Thus, (36) holds for all . Now

Theorem 24 (boundedness). *Let be a collection of HFLNs. If , , where and , then
*

*Proof. *Let , and then .

Since for all , we have
and then
Similarly,