Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Guan, Xin; Sun, Guidong; Yi, Xiao; Zhao, Jing

doi:https://doi.org/10.1155/2018/7436054

Mathematical Problems in Engineering

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Abstract Introduction Preliminaries Conclusion Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2018 | Article ID 7436054 | https://doi.org/10.1155/2018/7436054

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Xin Guan,¹Guidong Sun,¹Xiao Yi,¹and Jing Zhao¹

Academic Editor: Peide Liu

Received16 Oct 2017

Revised11 Mar 2018

Accepted25 Mar 2018

Published06 May 2018

Abstract

Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

1. Introduction

Multiattribute decision-making (MADM) is pervasive and active around human beings’ practical activities. It is an effect and basic method to solve large quantitative and qualitative problems as information fusion, pattern recognition, alternatives selection and evaluation, clustering analysis, military applications, and so forth. However, with the increasing complexity of the decision-making’s environments, the attributes tend to be more and more uncertain. The traditional decision-making methods cannot address these conditions. Therefore, the fuzzy multiattribute decision-making (FMADM) is introduced and widely used to tackle this uncertainty since Zadeh [1] initially proposed the theory of fuzzy set in 1965.

Many researchers have devoted themselves to the FMADM with different types of fuzzy sets, from the traditional fuzzy set to the intuitionistic fuzzy sets (IFSs) and hesitant fuzzy sets (HFSs). IFSs and HFSs are the two most popular fuzzy sets at present, which have been extensively paid attention to. Xu [2] firstly introduced the intuitionistic fuzzy weighted averaging (IFWA) and intuitionistic fuzzy ordered weighted averaging (IFOWA) operators to make the decision. Further, Zhou et al. [3–6] extended these operators by different measures of the IFSs. Recently, Liu et al. [7–15] provided some newest achievements of IFWA and IFOWA operators for IFSs and their extensions by using different kinds of aggregation methods to deal with FMADM, which greatly perfect this theory.

As to the HFS and its extensions, it is a relative new type of fuzzy set which we mainly focus on in this paper. It also attracted great interest from scholars all over the world. Xu and Xia [16, 17], Farhadinia [18, 19], Zeng et al. [20], Zhao et al. [21], Chen et al. [22], and Liao et al. [23] provided a variety of distance, similarity, entropy, and correlation measures for HFSs. Xia et al. [24, 25] and Liao et al. [26, 27] also presented some basic operations and aggregation operators for HFSs. With the help of these basic information measures and aggregation operators, decision can be made with hesitant fuzzy information. He et al. [28] first introduced the expected value and the geometric average value of hesitant multiplicative element (HME) to group decision-making problems. Xu and Zhang [29] developed a novel approach based on TOPSIS and the maximizing deviation method for solving MADM problems with hesitant fuzzy information. Further, Sun et al. [30] constructed an innovative TOPSIS based on synthetic correlation coefficient between HFSs which can handle negative values. Zhang and Xu [31] proposed an interval programming method for solving MAGDM problems with hesitant fuzzy alternatives based on LINMAP. Ashtiani and Azgomi [32] proposed a hesitant FMADM based computational trust model capable of taking into account the fundamental building blocks corresponding to the concept of trust. Ebrahimpour and Eftekhari [33] proposed an innovative method to deal with feature subset selection with HFSs based on maximum relevancy and minimum redundancy approach. Rodríguez et al. [34] introduced the concept of a hesitant fuzzy linguistic term set (HFLTS) to provide a linguistic and computational basis. Liao et al. [35] developed a method to solve the MCDM problem within the context of HFLTSs. Wang et al. [36] developed a likelihood-based TODIM approach for the selection and evaluation with multihesitant fuzzy linguistic information. Meng et al. [37] presented a similarity measure for uncertain linguistic hesitant fuzzy sets and constructed the optimal weight vector for multiattribute decision-making for evaluating corporate environmental performance. Feng et al. [38] proposed a consistency measure method based on the hesitant goal programming model to define the consistency hesitant fuzzy linguistic preference relations. Wu and Xu [39] proposed a large-scale group decision-making consensus model with possibility distribution based hesitant fuzzy preference. Li et al. [40] personalized individual semantics in group decision-making with hesitant fuzzy linguistic terms sets by consensus model. Zhang et al. [41] defined three kinds of additive consistency indices to measure the consistency level of hesitant fuzzy preference relation. Li et al. [42] used the modified TODIM and prospect theory to solve MADM issue of mineral resources evaluation and resources management with hesitant fuzzy linguistic information. Xue and Du [43] defined the multiplicative consistency of hesitant fuzzy preference relations to relax the same number for all elements in HFS and made the decision by fuzzy linear programming method. Liu et al. [44] used the continuous entropy weights and improved Hamacher information aggregation operators to aggregate interval-valued hesitant fuzzy information. Yang et al. [45] used the possibility degree to present a new comparative law for MADM problems with interval-valued hesitant fuzzy soft sets.

Through the above analysis, it is shown that HFSs and their extensions have been effective fuzzy tools for decision-making with uncertainty. However, despite the qualitative and quantitative studies of HFSs, the present work for them mainly focuses on such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Actually, the traditional grey relational analysis of the fuzzy set takes an important occupation in the fuzzy measure field. It can measure the closeness of two fuzzy sets just like the distance and similarity measures. Many researchers have focused on the grey relational analysis of fuzzy set and proposed several approaches to solve MADM problems. Compared with IFSs, Wei [46, 47], Zhang et al. [48], and Guo [49] had systematically established a series of grey relational analysis (GRA) methods to investigate the MADM problems with intuitionistic fuzzy information. Meanwhile the grey relational analysis for HFSs is limited. Although Li and Wei [50], Sun et al. [51], Wang [52], and Zang et al. [53] have applied GRA for HFSs, DHFSs, and IVDHFSs, the grey relational degrees of them are only a partial measure of the HFSs, which only consider the range or distance between HFSs data. The effects of these grey relational degrees are actually similar to the distance and similarity measures, which limit their applications. Sun et al. [51] considered the slope of the HFSs to form a synthetic grey relational degree, but the combination of this degree is simple and cannot reflect the influence of the whole index space. For these reasons, in this paper, we aim to improve the existing GRA for HFSs and develop a novel grey relational degree that considers both the distance and slope of the HFSs data. The improved grey relational degree satisfies the bidirectional deviation and reflects the influence of the whole index space, which is more reasonable in applications.

Consequently, the motivation of this paper is to explore a novel synthetic grey relational degree for HFSs and develop a methodology based on TOPSIS to solve MADM problems with HFSs information. The novelty of this paper lies in three aspects: (1) exploring the slope grey relational coefficient and slope grey relational degree of the HFSs, (2) constructing the synthetic grey relational coefficient and synthetic grey relational degree of the HFSs by taking into account the distance and slope of the HFSs data, and (3) developing a grey relational based MADM methodology with HFSs information.

The rest of the paper is organized as follows: Section 2 briefly reviews the concepts of HFSs and grey relational analysis theory. In Section 3, we define the grey relational coefficient and grey relational degree for HFSs. We also improve the slope grey relational degree by defining the difference and slope of the HFSs. With the help of the grey relational degree and slope grey relational degree, we construct the synthetic grey relational degree by combining them. In Section 4, we develop a hesitant fuzzy MADM methodology based on the grey relational analysis for HFSs. In Section 5, we apply the proposed grey relational hesitant fuzzy MADM methodology to the practical MADM problems. Finally, the paper ends with some concluding remarks and future challenges in Section 6.

2. Preliminaries

In this section, we recall the HFSs and grey relational analysis theory.

2.1. Hesitant Fuzzy Sets

When an expert makes a decision, he may hesitate to choose the exact membership degree in . For such a circumstance where there are several membership degrees of one element to a set, Torra [54] developed the hesitant fuzzy set (HFS), which is a kind of generalized fuzzy set where the membership degree of an element to a certain set can be illustrated as several different values between 0 and 1. HFS is good at dealing with the situations where people have disagreements or hesitancy when deciding something.

Definition 1 (see [54]). Suppose that is a reference set; a hesitant fuzzy set (HFS) on is defined in terms of a function which is 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 the set . For convenience, Xia and Xu [24] call a hesitant fuzzy element (HFE), which is a basic unit of HFS.

2.2. Grey Relational Analysis Theory

Grey relational theory was originally introduced by Deng [55]. The traditional grey relational theory describes the closeness of two variables, which is necessary in the decision-making fields. It has been widely applied to decision-making, pattern recognition, and some other problems under uncertainty, particularly under the discrete data and fuzzy information.

Definition 2 (see [55]). For reference set and , the grey relational coefficient is defined bywhere is the identification coefficient, , and generally we let .
The grey relational degree is defined asTake the weight into consideration, let the weight vector of be , , , and the grey relational degree is extended to the weighted grey relational degree:

3. Grey Relational Analysis for HFSs

In this section, we apply grey relational theory to the HFSs and define some novel HFSs expressions based on grey relational theory.

3.1. Grey Relational Definition of HFEs and HFSs

Definition 3. For two hesitant fuzzy sets on the fixed set , and , with and , , ; then we define the grey relational coefficient between HFEs and aswhere is the distance between HFEs and , which can be calculated according to following equation: actually, a variety of distance measures for HFEs have been proposed; for more details, please refer to [16–20].
Based on grey relational coefficient between HFEs, the grey relational degree between HFSs and is defined as

In practical applications, the elements in the universe have different weights. Take the weight into consideration and let the weight vector of be , , , and we extend the HFSs grey relational degree to the weighted HFSs grey relational degree as

3.2. Slope Grey Relational Definition for HFEs and HFSs

The HFSs grey relational degree in Section 3.1 mainly focuses on the closeness of the two HFSs; here we improve a novel HFSs grey relational degree called HFSs slope grey relational degree to represent the linear fashion of HFSs. As a departure, we introduce two new concepts of HFEs and HFSs based on Sun et al.’s [51] definition of the difference and slope of the HFSs.

Definition 4. For hesitant fuzzy sets with on the fixed set , we define the difference of the HFSs aswhere denotes the difference of the HFEs.where

Definition 5. For hesitant fuzzy sets with on the fixed set , the difference of is with ; we define the slope of the HFSs aswhere denotes the slope of the HFEs.where where is the mean of the HFE .The difference and slope of the HFSs can denote the linear fashion of the HFSs clearly, which are useful in practice. With the help of these definitions, we can give the definition of the slope grey relational degree.

Definition 6. For two hesitant fuzzy sets on the fixed set , and with and , , ; the differences of them are , , and , , and , , and , ; the slopes of them are with and with ; we define the slope grey relational coefficient between the HFEs and aswhere Based on HFSs slope grey relational coefficient, the HFSs slope grey relational degree is defined asTake the weight into consideration; let the weight vector of be , , , and we extend the HFSs slope grey relational degree to the weighted HFSs slope grey relational degree as

3.3. Synthetic Grey Relational Definition for HFSs

Definition 7. For two hesitant fuzzy sets on the fixed set , and with and ; the slopes of them are with and with , , ; then we define the synthetic grey relational coefficient between HFEs and aswhere , which indicates the importance of the closeness and linear fashion of HFSs, respectively, which satisfied and denote the distinguished coefficient of the closeness and linear fashion, and are the distance between HFEs and and the distance between the slopes of HFEs and , respectively, and can be calculated byIf the numbers of values in different HFEs of HFSs are different, we have to extend the shorter one until both of them have the same length when we compare them. We can extend them according to the optimistic or the pessimistic methods in [7, 8].
Based on HFEs synthetic grey relational coefficient, the HFSs synthetic grey relational degree is defined asTake the weight into consideration; let the weight vector of be , , , and we extend the HFSs synthetic grey relational degree to the weighted HFEs synthetic grey relational degree asThe HFSs synthetic grey relational degree takes the considerations of both the closeness and linear fashion of HFSs together, which can better represent the fuzzy measure between the HFSs.

4. The Grey Relational Based MADM Methodology with HFSs Information

In this section, we investigate the grey relational analysis to MADM problems with HFSs information and propose the grey relational based MADM methodology with the help of the TOPSIS method.

Suppose that a hesitant fuzzy MADM problem has alternatives ; each alternative has hesitant fuzzy attribute ; represents the hesitant fuzzy information of the alternatives on the attribute ; is the number of the membership values in ; let be the relative weight vector of the attribute, satisfying the normalization conditions: and . Then all the hesitant fuzzy information can be concisely expressed in matrix format as

Then, according to the TOPSIS approach, we propose the grey relational based MADM methodology with hesitant fuzzy matrix as follows.

Step 1. Determine the positive ideal solution (PIS) and negative ideal solution (NIS) of each attribute in the normalized hesitant fuzzy decision matrix to form two new positive and negative HFSs: where and are the new positive and negative HFEs:where and are related to benefit attribute and cost attribute, and are the numbers of the membership values in the new positive and negative HFEs, respectively, and .

Step 2. Calculate the HFSs positive and negative grey relational degrees between each alternative and the PIS and NIS, respectively. Here, we can calculate them by the proposed three grey relational degrees: the HFSs grey relational degree, HFSs slope grey relational degree, and HFSs synthetic grey relational degree. where (27) can be obtained according to Definitions 3, 6, and 7.

Step 3. Construct the relative closeness to the ideal solution based on the calculated positive and negative grey relational degree. The relative closeness of the alternative with respect to the ideal solution is defined as

Step 4. Rank the alternatives according to the decreasing order of their relative closeness. That is, the best alternative is the one with the greatest relative closeness to the ideal solution.

The process of the grey relational based MADM methodology with hesitant fuzzy information is shown in Figure 1.

5. MADM Applications

In this section, we apply the proposed grey relational based MADM methodology to deal with MADM problems with HFSs information.

5.1. Apply the Proposed Grey Relational Based MADM Methodology to Energy Policy Selection

In this section, the MADM example with hesitant fuzzy information concerning energy policy selection in [16, 18, 29, 56] is used to validate the proposed grey relational based MADM methodology.

Example 8. Suppose that there are five alternatives (energy projects) to be selected and four attributes to be considered: . The attribute weight vector is . The attribute values are described in hesitant fuzzy sets, shown in Table 1. We want to get the most desirable alternative from the hesitant fuzzy information.
Because the numbers of values in different HFEs of HFSs are different, we cannot directly process this hesitant fuzzy information. We have to extend the shorter one until both of them have the same length when we compare them. In this example, we extend these data in the pessimistic view as [29] does. Actually, the extension of the HFSs plays an important role in the ultimate decision-making; different extension methods may obtain different results, so how to select the appropriate extension methods remains to be further research. The extended hesitant fuzzy decision-making information is shown in Table 2.

Step 1. All the attributes are of benefit type; we select each maximum HFE in the five alternatives HFSs on the four attributes to construct the hesitant fuzzy PIS and each minimum HFE to construct the hesitant fuzzy NIS :

Step 2. Calculate the three types of HFSs positive and negative grey relational degrees between each alternative and the PIS and NIS, respectively. We assume that , and . The results are shown in Table 3.

Step 3. Construct the relative closeness to the ideal solution based on the above calculated three types of positive and negative grey relational degree. The relative closeness of the alternative with respect to the ideal solution is shown in Table 4 and the compared effect of the three methods is shown in Figure 2.

Step 4. Rank the alternatives according to the decreasing order of the three types of relative closeness, shown in Table 4 too.

Consequently, we select the best one with the greatest relative closeness to the ideal solution and all the three types of relative closeness indicate that the decision result is alternative .

Comparing the decision results, we can see that the decision result is consistent with the decision result in [16, 18, 28, 56], derived from different distance and similarity measures. It is proven that the three types of grey relation methods are effective in the hesitant fuzzy decision problems, successfully applying the grey relation analysis theory into the hesitant fuzzy domain.

5.2. Apply the Proposed Grey Relational Based MADM Methodology to Multisensor Target Recognition

Example 9. Consider a multisensor target recognition problem. Three sensors detect an uncertain target with three different electromagnetic radiation features, respectively: pulse repetition frequency (PRF), carrier frequency (CF), and pulse width (PW). For the uncertainty of these data, these data are reported by HFSs. We need to find which target in the database matches the detected one. It is a MADM problem. We use the proposed grey relational based MADM methodology to recognize it in the database. The detected target and known targets data in the database are shown in Table 5.

The attribute weight vector of the electromagnetic radiation features is . In order to show the advantages of the proposed method, we use five HFSs measurements methods to compare the recognition result: the Hamming distance in [16], the correlation coefficient in [22], the grey relative degree, the slope grey relative degree, and synthetic grey relative degree proposed in our paper. The recognition data and recognition effect of these five methods are shown in Table 6 and Figure 3.

From Table 6 and Figure 3, we can see that different measurements methods result in different results. The similarity method and grey relative degree method regard known targets 3 and 4 as the best recognition result, while the correlation coefficient and slope grey relative degree refer the best recognition result to known targets 1 and 2. These two kinds of result are completely different. The reason is that the similarity method and grey relative degree method pay more attention to the closeness of the data and the correlation coefficient and slope grey relative degree method pay more attention to the linear relationship of the data. If the closeness of the data is equal, the similarity method and grey relative degree method get the same result. However, the patterns of known targets 3 and 4 are not the same. Under this condition, the similarity method and grey relative degree method cannot recognize which one is the recognition result. Similarly, if linear relationship of the data is equal, the correlation coefficient and slope grey relative degree method get the same result. But the patterns of known targets 1 and 2 are not the same. Under this condition, the correlation coefficient and slope grey relative degree method cannot get the correct recognition result. These two kinds of methods only consider one aspect of the real data, either the closeness or the linear relationship. They cannot distinguish which one is the correct result. Obviously, this will result in the unreasonable result, which is only partial one.

However, the synthetic grey relative degree takes the two factors, closeness and linear relationship, into consideration; it determines that only known target 2 is the recognition result. This result is more convincing than the others with its superiority in comprehensiveness and discrimination. Therefore, according to the above analysis and comparison, we make the decision that the detected target is recognized to be known target 2.

Combined with two practical MADM examples about energy policy selection and multisensor target recognition with HFSs information, we can see that the proposed grey relational based MADM methodology can deal with the HFSs MADM problems well. In addition, the decision results are the same as the previous methods, which demonstrates the grey relational based MADM methodology’s effectiveness. Furthermore, it is the first time to construct the synthetic grey relational degree for HFSs by considering both the closeness and the slope factors of HFSs data, which is significant in the development of the HFSs by enriching their fuzzy measures.

6. Conclusion

In this paper, we apply the grey relational analysis theory to the HFSs domain. We improve the traditional grey relational degree for HFSs by a novel slope grey relational degree which considers the slope factor of the HFSs data. We further construct the synthetic grey relational degree for HFSs which takes both the distance and slope factors of the HFSs data into account. This synthetic grey relational degree can better describe the fuzzy measures than the traditional grey relational degree. Additionally, we also develop the HFSs grey relational based MADM methodology based on the TOPSIS method to solve the HFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the appropriate decision results. Comparing the decision results with the previous methods, the validity and accuracy of the proposed MADM methodology based on the synthetic grey relational degree are illustrated.

In the future, we will apply the proposed synthetic grey relational degree to other HFSs extensions as the interval hesitant fuzzy sets, dual hesitant fuzzy sets, hesitant fuzzy linguistic sets, and so forth.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work is supported by the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China, the Special Fund for the Taishan Scholar Project (Grant no. ts201712072), the Major Research Plan of the National Natural Science Foundation of China (Grant no. 91538201), the National Natural Science Foundation of China (Grants nos. 61671463 and 61571454), the Program for New Century Excellent Talents in University (Grant no. NCET-11-0872), and the Natural Science Foundation of Shandong Province (Grant no. ZR2017BG014).

References

L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
View at: Publisher Site | Google Scholar
Z. Xu, “Intuitionistic fuzzy aggregation operators,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 6, pp. 1179–1187, 2007.
View at: Publisher Site | Google Scholar
L. Zhou, J. M. Merigó, H. Chen, and J. Liu, “The optimal group continuous logarithm compatibility measure for interval multiplicative preference relations based on the COWGA operator,” Information Sciences, vol. 328, pp. 250–269, 2016.
View at: Publisher Site | Google Scholar
L. Zhou, F. Jin, H. Chen, and J. Liu, “Continuous intuitionistic fuzzy ordered weighted distance measure and its application to group decision making,” Technological and Economic Development of Economy, vol. 22, no. 1, pp. 75–99, 2016.
View at: Publisher Site | Google Scholar
L. Zhou, Z. Tao, H. Chen, and J. Liu, “Intuitionistic fuzzy ordered weighted cosine similarity measure,” Group Decision & Negotiation, vol. 23, no. 4, pp. 879–900, 2014.
View at: Publisher Site | Google Scholar
L. Zhou, Z. Tao, H. Chen, and J. Liu, “Continuous interval-valued intuitionistic fuzzy aggregation operators and their applications to group decision making,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, vol. 38, no. 7-8, pp. 2190–2205, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
P. Liu and S.-M. Chen, “Multiattribute group decision making based on intuitionistic 2-tuple linguistic information,” Information Sciences, vol. 430/431, pp. 599–619, 2018.
View at: Publisher Site | Google Scholar | MathSciNet
P. Liu and P. Wang, “Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making,” International Journal of Intelligent Systems, vol. 33, no. 2, pp. 259–280, 2018.
View at: Publisher Site | Google Scholar
P. Liu and J. Liu, “Some q-Rung Orthopai Fuzzy Bonferroni Mean Operators and Their Application to Multi-Attribute Group Decision Making,” International Journal of Intelligent Systems, vol. 33, no. 2, pp. 315–347, 2018.
View at: Publisher Site | Google Scholar
P. Liu, J. Liu, and J. M. Merigó, “Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making,” Applied Soft Computing, vol. 62, pp. 395–422, 2018.
View at: Publisher Site | Google Scholar
P. Liu, J. Liu, and S.-M. Chen, “Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making,” Journal of the Operational Research Society, vol. 69, no. 1, pp. 1–24, 2018.
View at: Google Scholar
P. D. Liu, “Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power heronian aggregation operators,” Computers & Industrial Engineering, vol. 108, pp. 199–212, 2017.
View at: Publisher Site | Google Scholar
P. Liu and S.-M. Chen, “Group Decision Making Based on Heronian Aggregation Operators of Intuitionistic Fuzzy Numbers,” IEEE Transactions on Cybernetics, vol. 47, no. 9, pp. 2514–2530, 2017.
View at: Google Scholar
P. Liu, S.-M. Chen, and J. Liu, “Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators,” Information Sciences, vol. 411, pp. 98–121, 2017.
View at: Publisher Site | Google Scholar | MathSciNet
P. Liu and H. Li, “Interval-Valued Intuitionistic Fuzzy Power Bonferroni Aggregation Operators and Their Application to Group Decision Making,” Cognitive Computation, vol. 9, no. 4, pp. 494–512, 2017.
View at: Google Scholar
Z. S. Xu and M. M. Xia, “Distance and similarity measures for hesitant fuzzy sets,” Information Sciences, vol. 181, no. 11, pp. 2128–2138, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
Z. S. Xu and M. M. Xia, “Hesitant fuzzy entropy and cross-entropy and their use in multiattribute decision-making,” International Journal of Intelligent Systems, vol. 27, no. 9, pp. 799–822, 2012.
View at: Publisher Site | Google Scholar
B. Farhadinia, “Distance and similarity measures for higher order hesitant fuzzy sets,” Knowledge-Based Systems, vol. 55, pp. 43–48, 2014.
View at: Publisher Site | Google Scholar
B. Farhadinia, “Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets,” Information Sciences, vol. 240, pp. 129–144, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
W. Zeng, D. Li, and Q. Yin, “Distance and similarity measures between hesitant fuzzy sets and their application in pattern recognition,” Pattern Recognition Letters, vol. 84, pp. 267–271, 2016.
View at: Publisher Site | Google Scholar
N. Zhao, Z. Xu, and F. Liu, “Uncertainty measures for hesitant fuzzy information,” International Journal of Intelligent Systems, vol. 30, no. 7, pp. 818–836, 2015.
View at: Publisher Site | Google Scholar
N. Chen, Z. Xu, and M. Xia, “Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, vol. 37, no. 4, pp. 2197–2211, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
H. Liao, Z. Xu, and X.-J. Zeng, “Novel correlation coefficients between hesitant fuzzy sets and their application in decision making,” Knowledge-Based Systems, vol. 82, article no. 3081, pp. 115–127, 2015.
View at: Publisher Site | Google Scholar
M. Xia and Z. Xu, “Hesitant fuzzy information aggregation in decision making,” International Journal of Approximate Reasoning, vol. 52, no. 3, pp. 395–407, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
M. Xia, Z. Xu, and N. Chen, “Some hesitant fuzzy aggregation operators with their application in group decision making,” Group Decision and Negotiation, vol. 22, no. 2, pp. 259–279, 2013.
View at: Publisher Site | Google Scholar
H. Liao and Z. Xu, “Subtraction and division operations over hesitant fuzzy sets,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 27, no. 1, pp. 65–72, 2014.
View at: Publisher Site | Google Scholar
H. Liao, Z. Xu, and M. Xia, “Multiplicative consistency of hesitant fuzzy preference relation and its application in group decision making,” International Journal of Information Technology & Decision Making, vol. 13, no. 1, pp. 47–76, 2014.
View at: Publisher Site | Google Scholar
Y. He, Z. Xu, and J. Gu, “An approach to group decision making with hesitant information and its application in credit risk evaluation of enterprises,” Applied Soft Computing, vol. 43, pp. 159–169, 2016.
View at: Publisher Site | Google Scholar
Z. Xu and X. Zhang, “Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information,” Knowledge-Based Systems, vol. 52, pp. 53–64, 2013.
View at: Publisher Site | Google Scholar
G. Sun, X. Guan, X. Yi, and Z. Zhou, “An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications,” Applied Soft Computing, vol. 68, pp. 249–267, 2018.
View at: Google Scholar
X. Zhang and Z. Xu, “Interval programming method for hesitant fuzzy multi-attribute group decision making with incomplete preference over alternatives,” Computers & Industrial Engineering, vol. 75, no. 1, pp. 217–229, 2014.
View at: Publisher Site | Google Scholar
M. Ashtiani and M. A. Azgomi, “A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty,” Applied Soft Computing, vol. 42, pp. 18–37, 2016.
View at: Publisher Site | Google Scholar
M. K. Ebrahimpour and M. Eftekhari, “Ensemble of feature selection methods: A hesitant fuzzy sets approach,” Applied Soft Computing, vol. 50, pp. 300–312, 2017.
View at: Publisher Site | Google Scholar
R. M. Rodríguez, L. Martinez, and F. Herrera, “Hesitant fuzzy linguistic term sets for decision making,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 109–119, 2012.
View at: Publisher Site | Google Scholar
H. Liao, Z. Xu, and X.-J. Zeng, “Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1343–1355, 2015.
View at: Publisher Site | Google Scholar
J. Wang, J. Wang, and H. Zhang, “A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing,” Computers & Industrial Engineering, vol. 99, pp. 287–299, 2016.
View at: Publisher Site | Google Scholar
F. Meng, J. Tang, and C. Li, “Uncertain linguistic hesitant fuzzy sets and their application in multi-attribute decision making,” International Journal of Intelligent Systems, vol. 33, pp. 586–614, 2018.
View at: Google Scholar
X. Feng, L. Zhang, and C. Wei, “The consistency measures and priority weights of hesitant fuzzy linguistic preference relations,” Applied Soft Computing, vol. 65, pp. 79–90, 2018.
View at: Publisher Site | Google Scholar
Z. Wu and J. Xu, “A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters,” Information Fusion, vol. 41, pp. 217–231, 2018.
View at: Google Scholar
C. Li, R. M. Rodríguez, L. Martínez, Y. Dong, and F. Herrera, “Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions,” Knowledge-Based Systems, vol. 145, pp. 156–165, 2018.
View at: Publisher Site | Google Scholar
Z. Zhang, X. Kou, and Q. Dong, “Additive consistency analysis and improvement for hesitant fuzzy preference relations,” Expert Systems With Applications, vol. 98, pp. 118–128, 2018.
View at: Google Scholar
P. Li, X. Chen, X. Qu, and Q. Xu, “The Evaluation of Mineral Resources Development Efficiency Based on Hesitant Fuzzy Linguistic Approach and Modified TODIM,” Mathematical Problems in Engineering, vol. 2018, pp. 1–9, 2018.
View at: Publisher Site | Google Scholar
M. Xue and Y. Du, “A group decision-making model based on regression method with hesitant fuzzy preference relations,” Mathematical Problems in Engineering, Article ID 6549791, 8 pages, 2017.
View at: Publisher Site | Google Scholar | MathSciNet
J. Liu, N. Zhou, L.-H. Zhuang, N. Li, and F.-F. Jin, “Interval-Valued Hesitant Fuzzy Multiattribute Group Decision Making Based on Improved Hamacher Aggregation Operators and Continuous Entropy,” Mathematical Problems in Engineering, vol. 2017, Article ID 2931482, 2017.
View at: Publisher Site | Google Scholar
Y. Yang, L. Lang, L. Lu, and Y. Sun, “A new method of multiattribute decision-making based on interval-valued hesitant fuzzy soft sets and its application,” Mathematical Problems in Engineering, Article ID 9376531, 8 pages, 2017.
View at: Publisher Site | Google Scholar | MathSciNet
G. Wei, “GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting,” Knowledge-Based Systems, vol. 23, no. 3, pp. 243–247, 2010.
View at: Publisher Site | Google Scholar
G. Wei, “Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making,” Expert Systems with Applications, vol. 38, no. 9, pp. 11671–11677, 2011.
View at: Publisher Site | Google Scholar
S. Zhang, S. Liu, and R. Zhai, “An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights,” Computers & Industrial Engineering, vol. 61, no. 4, pp. 1336–1341, 2011.
View at: Publisher Site | Google Scholar
J. Guo, “Hybrid multiattribute group decision making based on intuitionistic fuzzy information and GRA method,” ISRN Applied Mathematics, vol. 2013, Article ID 146026, 10 pages, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
X. Li and G. Wei, “GRA method for multiple criteria group decision making with incomplete weight information under hesitant fuzzy setting,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 27, no. 3, pp. 1095–1105, 2014.
View at: Publisher Site | Google Scholar
G. Sun, X. Guan, X. Yi, and Z. Zhou, “Grey relational analysis between hesitant fuzzy sets with applications to pattern recognition,” Expert Systems with Applications, vol. 92, pp. 521–532, 2018.
View at: Publisher Site | Google Scholar
Y.-X. Wang, “Research on the efficiency evaluation of venture capital of high-science and technology companies with dual hesitant fuzzy information,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 31, no. 3, pp. 1183–1187, 2016.
View at: Publisher Site | Google Scholar
Y. Zang, W. Sun, and S. Han, “Grey relational projection method for multiple attribute decision making with interval-valued dual hesitant fuzzy information,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 33, no. 2, pp. 1053–1066, 2017.
View at: Publisher Site | Google Scholar
V. Torra, “Hesitant fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529–539, 2010.
View at: Publisher Site | Google Scholar
J. L. Deng, “Introduction to grey system theory,” The Journal of Grey System, vol. 1, no. 1, pp. 1–24, 1989.
View at: Google Scholar | MathSciNet
S.-C. Ngan, “A unified representation of intuitionistic fuzzy sets, hesitant fuzzy sets and generalized hesitant fuzzy sets based on their u-maps,” Expert Systems with Applications, vol. 69, pp. 257–276, 2017.
View at: Publisher Site | Google Scholar

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Copyright © 2018 Xin Guan 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.

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