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Mathematical Problems in Engineering

Volume 2015, Article ID 254764, 17 pages

http://dx.doi.org/10.1155/2015/254764

## On Interval-Valued Hesitant Fuzzy Soft Sets

^{1}School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou, Gansu 730030, China^{2}School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan 650500, China

Received 16 October 2014; Revised 13 January 2015; Accepted 19 January 2015

Academic Editor: Shuming Wang

Copyright © 2015 Haidong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

By combining the interval-valued hesitant fuzzy set and soft set models, the purpose of this paper is to introduce the concept of interval-valued hesitant fuzzy soft sets. Further, some operations on the interval-valued hesitant fuzzy soft sets are investigated, such as complement, “AND,” “OR,” ring sum, and ring product operations. Then, by means of reduct interval-valued fuzzy soft sets and level hesitant fuzzy soft sets, we present an adjustable approach to interval-valued hesitant fuzzy soft sets based on decision making and some numerical examples are provided to illustrate the developed approach. Finally, the weighted interval-valued hesitant fuzzy soft set is also introduced and its application in decision making problem is shown.

#### 1. Introduction

Soft sets, initiated by Molodtsov [1], are a new mathematical tool for dealing with uncertainties which are free from many difficulties that have troubled the usual theoretical approaches. It has been found that fuzzy sets, rough sets, and soft sets are closely related concepts [2]. Soft set theory has potential applications in many different fields including the smoothness of functions, game theory, operational research, Perron integration, probability theory, and measurement theory [1, 3].

Research works on soft sets develop very rapidly and now are one of hotspots in the uncertainty research. For example, Maji et al. [4] defined several operations on soft sets and made a theoretical study on the theory of soft sets. Jun [5] introduced the concept of soft BCK/BCI-algebras. Subsequently, they also discussed the applications of soft sets in ideal theory of BCK/BCI-algebras [6]. Feng et al. [7] applied soft set theory to the study of semirings and initiated the notion of soft semirings. Furthermore, based on [4], Ali et al. [8] introduced some new operations on soft sets and improved the notion of complement of soft set. They proved that certain De-Morgan’s laws hold in soft set theory. Qin and Hong [9] introduced the concept of soft equality and established lattice structures and soft quotient algebras of soft sets. Meanwhile, the study of hybrid models combining soft sets with other mathematical structures is also emerging as an active research topic of soft set theory. Yang et al. [10] introduced the interval-valued fuzzy soft sets by combining interval-valued fuzzy set with soft set models and analyzed a decision problem by the model. By using the multifuzzy set and soft set models, Yang et al. [11] presented the concept of the multifuzzy soft sets and provided its application in decision making under an imprecise environment. Maji et al. [12] initiated the study on hybrid structures involving fuzzy sets and soft sets. They introduced the notion of fuzzy soft sets, which can be seen as a fuzzy generalization of soft sets. Furthermore, based on [12], Roy and Maji [13] presented a novel method concerning object recognition from an imprecise multiobserver data so as to cope with decision making based on fuzzy soft sets. Then Kong et al. [14] revised the Roy-Maji method by considering “fuzzy choice values.” Subsequently, Feng et al. [15, 16] further discussed the application of fuzzy soft sets and interval-valued fuzzy soft sets to decision making in an imprecise environment. They proposed an adjustable approach to fuzzy soft sets and interval-valued fuzzy soft sets based decision making in [15, 16]. By introducing the interval-valued intuitionistic fuzzy sets into soft sets, Jiang et al. [17] defined the concept of interval-valued intuitionistic fuzzy soft set. Moreover, they also defined some operations on the interval-valued intuitionistic fuzzy soft sets and investigated some basic properties. On the basis of [17], Zhang et al. [18] developed an adjustable approach to decision making problems based on interval-valued intuitionistic fuzzy soft sets. Very recently, integrating trapezoidal fuzzy sets with soft sets, Xiao et al. [19] initiated the trapezoidal fuzzy soft sets to deal with certain linguistic assessments. Further, in order to capture the vagueness of the attribute with linguistic assessments information, Zhang et al. [20] generalized trapezoidal fuzzy soft sets introduced by Xiao et al. [19] and defined the concept of generalized trapezoidal fuzzy soft sets.

As a mathematical method to deal with vagueness in everyday life, fuzzy set was introduced by Zadeh in [21]. Up to present, several extensions have been developed, such as intuitionistic fuzzy set [22], interval valued fuzzy sets [23–25], type-2 fuzzy set [26, 27], and type- fuzzy set [26]. Recently, Torra and Narukawa [28, 29] extended fuzzy sets to hesitant fuzzy environment and initiated the notion of hesitant fuzzy sets (HFSs), because they found that because of the doubts between a few different values, it is very difficult to determine the membership of an element to a set under a group setting [29]. For example, two decision makers discuss the membership degree of into . One wants to assign 0.7, but the other wants to assign 0.9. They cannot persuade each other. To avoid an argument, the membership degrees of into can be described as . After it was introduced by Torra, the hesitant fuzzy set has attracted more and more scholars’ attention [30–32].

As mentioned above, there are close relationships among the uncertainty theories, such as fuzzy sets, rough sets, and soft sets. So many hybrid models among them are proposed by the researchers, such as fuzzy rough sets [33], rough fuzzy sets [33], and fuzzy soft sets [12]. Since the appearance of hesitant fuzzy set, the study on it has never been stopped. The combination of hesitant fuzzy set with other uncertainty theories is a hot spot of the current research recently. There are several hybrid models in present, such as hesitant fuzzy rough sets [34] and hesitant fuzzy soft sets [35, 36]. In fact, Babitha and John [35] defined a hybrid model called hesitant fuzzy soft sets and investigated some of their basic properties. Meanwhile, Wang et al. [36] also initiated the concept of hesitant fuzzy soft sets by integrating hesitant fuzzy set with soft set model and presented an algorithm to solve decision making problems based on hesitant fuzzy soft sets. By combining hesitant fuzzy set and rough set models, Yang et al. [34] introduced the concept of the hesitant fuzzy rough sets and proposed an axiomatic approach to the model.

However, Chen et al. [37, 38] pointed out that it is very difficult for decision makers to exactly quantify their ideas by using several crisp numbers because of the lack of available information in many decision making events. Therefore, Chen et al. [37, 38] extended hesitant fuzzy sets into interval-valued hesitant fuzzy environment and introduced the concept of interval-valued hesitant fuzzy sets (IVHFSs), which permits the membership degrees of an element to a given set to have a few different interval values. It should be noted that when the upper and lower limits of the interval values are identical, IVHFS degenerates into HFS, indicating that the latter is a special case of the former. Recently, similarity, distance, and entropy measures for IVHFSs have been investigated by Farhadinia [39]. Wei et al. [40] discussed some interval-valued hesitant fuzzy aggregation operators and gave their applications to multiple attribute decision making based on interval-valued hesitant fuzzy sets.

As a novel mathematical method to handle imprecise information, the study of hybrid models combining IVHFSs with other uncertainty theories is emerging as an active research topic of IVHFS theory. Meanwhile, we know that there are close relationships among the uncertainty theories, such as interval-valued hesitant fuzzy sets, rough sets, and soft sets. Therefore, many scholars have been starting to research on the area. For example, Zhang et al. [41] generalized the hesitant fuzzy rough sets to interval-valued hesitant fuzzy environment and presented an interval-valued hesitant fuzzy rough set model by integrating interval-valued hesitant fuzzy set with rough set theory. On the one hand, it is unreasonable to use hesitant fuzzy soft sets to handle some decision making problems because of insufficiency in available information. Instead, adopting several interval numbers may overcome the difficulty. In that case, it is necessary to extend hesitant fuzzy soft sets [36] into interval-valued hesitant fuzzy environment. On the other hand, by referring to a great deal of literature and expertise, we find that the discussions about fusions of interval-valued hesitant fuzzy set and soft sets do not also exist in the related literatures. Considering the above facts, it is necessary for us to investigate the combination of IVHFS and soft set. The purpose of this paper is to initiate the concept of interval-valued hesitant fuzzy soft set by combining interval-valued hesitant fuzzy set and soft set theory. In order to illustrate the efficiency of the model, an adjustable approach to interval-valued hesitant fuzzy soft sets based on decision making is also presented. Finally, some numerical examples are provided to illustrate the adjustable approach.

To facilitate our discussion, we first review some backgrounds on soft sets, fuzzy soft sets, hesitant fuzzy sets, and interval numbers in Section 2. In Section 3, the concept of interval-valued hesitant fuzzy soft set with its operation rules is presented. In Section 4, an adjustable approach to interval-valued hesitant fuzzy soft sets based on decision making is proposed. In Section 5, the concept of weighted interval-valued hesitant fuzzy soft sets is defined and applied to decision making problems in which all the decision criteria may not be of equal importance. Finally, we conclude the paper with a summary and outlook for further research in Section 6.

#### 2. Preliminaries

In this section, we briefly review the concepts of soft sets, fuzzy soft sets, hesitant fuzzy sets, and interval numbers. The pair will be called a soft universe. Throughout this paper, unless otherwise stated, refers to an initial universe, is a set of parameters, is the power set of , and .

##### 2.1. Soft Sets, Fuzzy Soft Sets, and Hesitant Fuzzy Sets

According to [1], the concept of soft sets is defined as follows.

*Definition 1 (see [1]). *A pair is called a soft set over , where is a mapping given by .

Combining fuzzy sets and soft sets, Maji et al. [12] initiated the following hybrid model called fuzzy soft sets, which can be seen as an extension of both fuzzy sets and crisp soft sets.

*Definition 2 (see [12]). *A pair is called a fuzzy soft set over if and , where is the set of all fuzzy subsets of .

In the following, we review some basic concepts related to hesitant fuzzy sets introduced by Torra [28, 29].

*Definition 3 (see [28, 29]). *Let be a fixed set; a hesitant fuzzy set (HFS, for short) on is in terms of a function that when applied to returns a subset of ; that is,where is a set of some different values in , representing the possible membership degrees of the element to .

For convenience, we call a hesitant fuzzy element (HFE, for short).

*Example 4. *Let be a reference set and let , , and be the HFEs of to a set , respectively. Then can be considered as a HFS; that is,

##### 2.2. Interval Numbers

In [42], Xu and Da gave the concept of interval numbers and further investigated some of their properties.

*Definition 5 (see [42]). *Let ; then is called an interval number. In particular, is a real number, if .

*Definition 6 (see [42]). *Let , , and ; then one has the following:(1), if and ,(2),(3). In particular, , if .

*Definition 7 (see [42]). *Let , and , and let and ; then the degree of possibility of is defined as

Similarly, the degree of possibility of is defined as

Equations (3) and (4) are proposed in order to compare two interval numbers and to rank all the input arguments. Further details could be found in [42].

#### 3. Interval-Valued Hesitant Fuzzy Soft Sets

##### 3.1. Concept of Interval-Valued Hesitant Fuzzy Sets

In the subsection, we review some basic concepts related to interval-valued hesitant fuzzy sets introduced by Chen et al. [37].

*Definition 8 (see [37]). *Let be a fixed set, and let be the set of all closed subintervals of . An interval-valued hesitant fuzzy set (IVHFS, for short) on is defined aswhere denotes all possible interval-valued membership degrees of the element to .

For convenience, we call an interval-valued hesitant fuzzy element (IVHFE, for short). The set of all interval-valued hesitant fuzzy sets on is denoted by . From Definition 8, we can note that an IVHFS can be seen as an interval-valued fuzzy set if there is only one element in , which indicates that interval-valued fuzzy sets are a special type of IVHFSs.

*Example 9. *Let be a reference set, and let and be the IVHFEs of to a set , respectively. Then can be considered as an IVHFS; that is,

It is noted that the number of interval values in different IVHFEs may be different and the interval values are usually out of order. Suppose that stands for the number of interval values in the IVHFE . To operate correctly, Chen et al. [37] gave the following assumptions.

(A1) All the elements in each IVHFE are arranged in increasing order by (3). Let stand for the th largest interval numbers in the IVHFE , whereis an interval number, and , represent the lower and upper limits of , respectively.

(A2) If two IVHFEs , , , then . To have a correct comparison, the two IVHFEs and should have the same length . If there are fewer elements in than in , an extension of should be considered optimistically by repeating its maximum element until it has the same length with .

Given three IVHFEs represented by , , and , Chen et al. [37] defined some operations on them as follows.

*Definition 10. *Let , , and be three IVHFEs; then one has the following:(1),(2),(3),(4),(5),(6),(7).

Further, Chen et al. [37] established some relationships for the above operations on IVHFEs.

Theorem 11. *Let , , and be three IVHFEs; one has the following:*(1)*,*(2)*,*(3)*,*(4)*,*(5)*,*(6)*,*(7)*,*(8)*.*

*Example 12. *Let and be two IVHFEs; then, by the operational laws of IVHFEs given in Definition 10, we have

*From Example 12, we can see that the dimension of the derived IVHFE may increase as the addition or multiplicative operations are done, which may increase the complexity of the calculations. To overcome the difficulty, we develop some new methods to decrease the dimension of the derived IVHFE when operating the IVHFEs on the premise of the assumptions given by Chen et al. [37]. The adjusted operational laws are defined as follows.*

*Definition 13. *Let , , and be three IVHFEs, and let be a positive real number; then one has the following:(1),(2),(3),(4),(5),(6),(7),

*
where is the th largest interval number in .*

*Theorem 14. Let , , and be three IVHFEs. For the new operations in Definition 13, one has the following:(1),(2),(3),(4),(5),(6),(7),(8).*

*Proof. *The proofs are similar to Theorems 1 and 2 in [37].

*Theorem 14 shows that Theorem 11 is still valid for the new operations in Definition 13.*

*Example 15. *Reconsider Example 12. By (3) and assumptions given by Chen et al. [37]; then and . By virtue of Definition 13, we have

*Comparing Example 12 with Example 15, we note that the adjusted operational laws given in Definition 13 indeed decrease the dimension of the derived IVHFE when operating the IVHFEs, which brings grievous advantage for the practicing application.*

*3.2. Concept of Interval-Valued Hesitant Fuzzy Soft Sets*

*3.2. Concept of Interval-Valued Hesitant Fuzzy Soft Sets*

*In [35, 36, 43], researchers have introduced the concept of hesitant fuzzy soft sets and developed some approaches to hesitant fuzzy soft sets based on decision making. However, incompleteness and inaccuracy of information in the process of making decision are very important problems we have to resolve. Due to insufficiency in available information, it is unreasonable for us to adopt hesitant fuzzy soft sets to deal with some decision making problems in which decision makers only quantify their opinions with several crisp numbers. Instead, the basic characteristics of the decision-making problems described by several interval numbers may overcome the difficulty. Based on the above fact, we extend hesitant fuzzy soft sets into the interval-valued hesitant fuzzy environment and introduce the concept of interval-valued hesitant fuzzy sets.*

*In this subsection, we first introduce the notion of interval-valued hesitant fuzzy soft sets, which is a hybrid model combining interval-valued hesitant fuzzy sets and soft sets.*

*Definition 16. *Let be a soft universe and . A pair is called an interval-valued hesitant fuzzy soft set over , where is a mapping given by .

*An interval-valued hesitant fuzzy soft set is a parameterized family of interval-valued hesitant fuzzy subsets of . That is to say, is an interval-valued hesitant fuzzy subset in , . Following the standard notations, can be written as*

*Sometimes we write as . If , we can also have an interval-valued hesitant fuzzy soft set .*

*Example 17. *Let be a set of four participants performing dance programme, which is denoted by . Let be a parameter set, where confident; creative; graceful}. Suppose that three judges think the precise membership degrees of a candidate to a parameter are hard to be specified. To overcome this barrier, they represent the membership degrees of a candidate to a parameter with several possible interval values. Then interval-valued hesitant fuzzy soft set defined as follows gives the evaluation of the performance of candidates by three judges: The tabular representation of is shown in Table 1.