Interval-valued Hesitant Fuzzy Information Research Articles

Multiple attribute group decision making based on interval-valued hesitant fuzzy information measures

Under the interval-valued hesitant fuzzy environment, we investigate a multiple attribute group decision making (MAGDM) method on the basis of some information measures. We first introduce three axiomatic definitions of information measures under interval-valued hesitant fuzzy environment, including the entropy, similarity measures and cross-entropy. Several information measure formulas for interval-valued hesitant fuzzy elements (IVHFEs) are further constructed, which is based on the continuous ordered weighted averaging (COWA) operator. Then, the relationship among the entropy, similarity measures and cross-entropy is discussed, from which we find that three information measures can be transformed by each other based on their axiomatic definitions. The programming model is established to determine optimal weight of attribute with the principle of minimum entropy and maximum cross-entropy. Furthermore, an approach to MAGDM is developed, in which the attribute values take the form of IVHFEs. Finally, a numerical example for emergency risk management (ERM) evaluation is provided to illustrate the application of the developed approach.

Multiple attribute group decision making based on IVHFPBMs and a new ranking method for interval-valued hesitant fuzzy information

The interval-valued hesitant fuzzy set is a significant tool to express the uncertain information. In this paper, we define the interval-valued hesitant fuzzy 2nd-order central polymerization degree (IVHFCP2) function and the interval-valued hesitant fuzzy 2nd-order dispersive central polymerization degree (IVHFDCP2) function to further compare different interval-valued hesitant fuzzy sets. To capture much more information for the multiple attribute group decision making, we combine the Bonferroni mean with the power average operator to accommodate to interval-valued hesitant fuzzy environments and develop the interval-valued hesitant fuzzy power Bonferroni mean (IVHFPBM) and the interval-valued hesitant fuzzy weighted power Bonferroni mean (IVHFWPBM). We investigate the desirable properties of the new interval-valued hesitant fuzzy aggregation operators and discuss their special cases in detail. Finally, the new aggregation operators are applied to interval-valued hesitant fuzzy multiple attribute group decision making and a numerical example is given to illustrate the effectiveness of the presented approaches.

An Improved Interval-Valued Hesitant Fuzzy Multi-Criteria Group Decision-Making Method and Applications

The Bonferroni mean (BM) can be used in situations where the aggregated arguments are correlated. BM is very useful for solving decision-making problems. For describing fuzziness and vagueness more accurately, the interval-valued hesitant fuzzy set (IVHFS), which is a generalization of the hesitant fuzzy set (HFS), can be used to describe the membership degrees with interval numbers. The aim of this paper is to propose the interval-valued hesitant fuzzy Bonferroni mean (IVHFBM) for aggregating interval-valued hesitant fuzzy information. Furthermore, the weighted form of IVHFBM (IVHFWBM) is forwarded and, hereby, a multi-criteria group decision-making (MCGDM) method is established. A case study on the problem of evaluating research funding applications in China is analyzed. A comparison between the proposed method and existing ones demonstrates its practicability.

Hesitant fuzzy multi-attribute decision-making based on the minimum deviation method

As a powerful and efficient tool in expressing the indeterminate and fuzzy information, the hesitant fuzzy set (HFS) has shown its increasing importance. It was first proposed by Torra and Narukawa, permitting the membership degree of an element to a set of several possible values. In this paper, based on the idea of minimum deviation between the subjective and the objective preferences, we first develop two methods to determine the attribute weights under the hesitant fuzzy environment. To do so, we present the concept of hesitant fuzzy expected value and then establish several optimization models to gain the attribute weights. After that, we use the information aggregation techniques to integrate the hesitant fuzzy attribute values or their expected values, and then sort the alternatives by the overall values. Moreover, we generalize these two methods to interval-valued HFSs, and a numerical example is utilized to show the detailed implementation procedure and effectiveness of our methods in solving multi-attribute decision-making problems with hesitant fuzzy or interval-valued hesitant fuzzy information.

Interval-valued hesitant fuzzy Einstein prioritized aggregation operators and their applications to multi-attribute group decision making

Interval-valued hesitant fuzzy information aggregation plays an important role in interval-valued hesitant fuzzy set theory, which has received more and more attention in recent years. In this paper, we investigate interval-valued hesitant fuzzy multi-attribute group decision-making problems in which there exists a prioritization relationship among the attributes. Firstly, we introduce some Einstein operational laws on interval-valued hesitant fuzzy sets, and discuss some relations of these operations. Then, we develop two interval-valued hesitant fuzzy prioritized aggregation operators with the help of Einstein operations, such as the interval-valued hesitant fuzzy Einstein prioritized weighted average (IVHFEPWA) operator and the interval-valued hesitant fuzzy Einstein prioritized weighted geometric (IVHFEPWG) operator, whose desirable properties are investigated in detail. We further analyze the relationship between these proposed operators and the existing interval-valued hesitant fuzzy prioritized aggregation operators. Moreover, an approach to interval-valued hesitant fuzzy multi-attribute group decision making is given on the basis of the proposed operators. Finally, a numerical example is provided to demonstrate their effectiveness.

An approach to multiple attributes decision making with hesitant interval-valued fuzzy information and its application

In this paper, we investigate the multiple attribute decision making (MADM) problems with hesitant interval-valued fuzzy information. We first introduce some operations on the hesitant interval-valued fuzzy sets. Then, we further develop some new Einstein aggregation operators based on the Choquet integral with hesitant interval-valued fuzzy information, such as the hesitant interval-valued fuzzy Einstein correlated averaging (HIVFECA) operator and hesitant interval-valued fuzzy Einstein correlated geometric (HIVFECG) operator. Then, we apply the hesitant interval-valued fuzzy Einstein correlated averaging (HIVFECA) operator and hesitant interval-valued fuzzy Einstein correlated geometric (HIVFECG) operator to deal with multiple attribute decision making under the hesitant interval-valued fuzzy environments. Finally, an illustrative example for evaluating the quality of physical education class in universal institutions is given to verify the developed approach.

Interval-Valued Hesitant Fuzzy Prioritized Weighted Aggregation Operators for Multiple Attribute Decision Making

The paper proposes an interval-valued hesitant fuzzy prioritized weighted averaging (IVHFPWA) operator and an interval-valued hesitant fuzzy prioritized weighted geometric (IVHFPWG) operator for aggregating interval-valued hesitant fuzzy information and investigates some properties of the proposed aggregation operators. Then, the IVHFPWA and IVHFPWG operators are utilized to develop two approaches for dealing with the interval-valued hesitant fuzzy multiple attribute decision making problems in which the attributes are in different priority level. Through the IVHFPWA or IVHFPWG operator and the score function of interval-valued hesitant fuzzy elements (IVHFEs), the ranking order of all alternatives can be determined and the best alternative can be easily identified as well. Finally, a practical example about talent introduction is given to demonstrate the practicality and effectiveness of the developed approaches.

Models for hesitant interval-valued fuzzy multiple attribute decision making based on the correlation coefficient with incomplete weight information

In this paper, we investigate the multiple attribute decision making problems with hesitant interval-valued fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of hesitant interval-valued fuzzy values. We first introduce some approaches to obtaining the weight information of attributes, and then establish an optimization model based on the positive ideal point of attribute values, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We compute the correlation coefficient between each alternative and positive ideal alternative, and then rank the alternatives by means of the correlation coefficient between each alternative and positive ideal alternative. Finally, we shall present a numerical example to show potential evaluation of emerging technology commercialization with hesitant interval-valued fuzzy information in order to illustrate the method proposed in this paper.

Interval-Valued Hesitant Fuzzy Hamacher Synergetic Weighted Aggregation Operators and Their Application to Shale Gas Areas Selection

We investigate the multiple criteria decision making (MCDM) problem concerns on the selection of shale gas areas with interval-valued hesitant fuzzy information. First, some Hamacher operations of interval-valued hesitant fuzzy information are introduced, which generalize and extend the existing ones. Then some interval-valued hesitant fuzzy Hamacher weighted aggregation operators, especially, the interval-valued hesitant fuzzy Hamacher synergetic weighted averaging (IVHFHSWA) operators and their geometric version (IVHFHSWG) operators that weight simultaneously the argument variables themselves and their position orders and thus generalize the ideas of the weighted averaging and the ordered weighted averaging, are proposed. The distinct advantages of these operators are that they can provide more choices for the decision makers and considerably enhance or deteriorate the performance of aggregation. The essential properties of these operators are studied and their specific cases are discussed. Based on the IVHFHSWA operator, we propose a practical approach to shale gas areas selection with interval-valued hesitant fuzzy information. Finally, an illustrative example for selecting the shale gas areas is used to demonstrate the practicality and effectiveness of the proposed approach and a comparative analysis is performed with other approaches to highlight the distinctive advantages of the proposed operators.

Approaches to multiple attribute decision making with hesitant interval-valued fuzzy information under correlative environment

Approaches to multiple attribute decision making with hesitant interval-valued fuzzy information under correlative environment

Continuous hesitant fuzzy aggregation operators and their application to decision making under interval-valued hesitant fuzzy setting.

Interval-valued hesitant fuzzy set (IVHFS), which is the further generalization of hesitant fuzzy set, can overcome the barrier that the precise membership degrees are sometimes hard to be specified and permit the membership degrees of an element to a set to have a few different interval values. To efficiently and effectively aggregate the interval-valued hesitant fuzzy information, in this paper, we investigate the continuous hesitant fuzzy aggregation operators with the aid of continuous OWA operator; the C-HFOWA operator and C-HFOWG operator are presented and their essential properties are studied in detail. Then, we extend the C-HFOW operators to aggregate multiple interval-valued hesitant fuzzy elements and then develop the weighted C-HFOW (WC-HFOWA and WC-HFOWG) operators, the ordered weighted C-HFOW (OWC-HFOWA and OWC-HFOWG) operators, and the synergetic weighted C-HFOWA (SWC-HFOWA and SWC-HFOWG) operators; some properties are also discussed to support them. Furthermore, a SWC-HFOW operators-based approach for multicriteria decision making problem is developed. Finally, a practical example involving the evaluation of service quality of high-tech enterprises is carried out and some comparative analyses are performed to demonstrate the applicability and effectiveness of the developed approaches.

Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making

In this paper, we develop a series of induced generalized aggregation operators for hesitant fuzzy or interval-valued hesitant fuzzy information, including induced generalized hesitant fuzzy ordered weighted averaging (IGHFOWA) operators, induced generalized hesitant fuzzy ordered weighted geometric (IGHFOWG) operators, induced generalized interval-valued hesitant fuzzy ordered weighted averaging (IGIVHFOWA) operators, and induced generalized interval-valued hesitant fuzzy ordered weighted geometric (IGIVHFOWG) operators. Next, we investigate their various properties and some of their special cases. Furthermore, some approaches based on the proposed operators are developed to solve multiple attribute group decision making (MAGDM) problems with hesitant fuzzy or interval-valued hesitant fuzzy information. Finally, some numerical examples are provided to illustrate the developed approaches.

Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making

In this paper, we investigate the multiple attribute decision making (MADM) problems in which attribute values take the form of hesitant interval-valued fuzzy information. Firstly, definition and some operational laws of hesitant interval-valued fuzzy elements and score function of hesitant interval-valued fuzzy elements are introduced. Then, we have developed some hesitant interval-valued fuzzy aggregation operators: hesitant interval-valued fuzzy weighted averaging (HIVFWA) operator, hesitant interval-valued fuzzy ordered weighted averaging (HIVFOWA) operator, the hesitant interval-valued fuzzy weighted geometric (HIVFWG) operator, hesitant interval-valued fuzzy ordered weighted geometric (HIVFOWG) operator, hesitant interval-valued fuzzy choquet ordered averaging (HIVFCOA) operator, hesitant interval-valued fuzzy choquet ordered geometric (HIVFCOG) operator, hesitant interval-valued fuzzy prioritized aggregation operators and hesitant interval-valued fuzzy power aggregation operators. We have applied the HIVFCOA and HIVFCOG operators to multiple attribute decision making with hesitant interval-valued fuzzy information. Finally an illustrative example has been given to show the developed method.

Induced hesitant interval-valued fuzzy Einstein aggregation operators and their application to multiple attribute decision making

In this paper, we investigate the multiple attribute decision making MADM problems with hesitant interval-valued fuzzy information. We first introduce some operations on hesitant fuzzy sets, such as Einstein sum, Einstein product, Einstein exponentiation, etc., and propose the hesitant interval-valued fuzzy sets. Then, we further develop some new Einstein aggregation operators with hesitant interval-valued fuzzy information, such as the hesitant interval-valued fuzzy Einstein weighted average HIVFEWA operator, hesitant interval-valued fuzzy Einstein weighted geometric HIVFEWG operator, hesitant interval-valued fuzzy Einstein ordered weighted average HIVFEOWA operator, hesitant interval-valued fuzzy Einstein ordered weighted geometric HIVFEOWG operator, induced hesitant interval-valued fuzzy Einstein ordered weighted averaging I-HIVFEOWA operator and induced hesitant interval-valued fuzzy Einstein ordered weighted geometric I-HIVFEOWG operator. Then, we apply the induced hesitant interval-valued fuzzy Einstein ordered weighted averaging I-HIVFEOWA operator and induced hesitant interval-valued fuzzy Einstein ordered weighted geometric I-HIVFEOWG operator to deal with multiple attribute decision making under the hesitant interval-valued fuzzy environments. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Interval-valued hesitant preference relations and their applications to group decision making

We introduce a new type of fuzzy preference structure, called interval-valued hesitant preference relations, to describe uncertain evaluation information in group decision making (GDM) processes. Moreover, it allows decision makers to offer all possible interval values that are not accounted for in current preference structure types when one compares two alternatives. We generalize the concept of hesitant fuzzy set (HFS) to that of interval-valued hesitant fuzzy set (IVHFS) in which the membership degrees of an element to a given set are not exactly defined, but denoted by several possible interval values. We give systematic aggregation operators to aggregate interval-valued hesitant fuzzy information. In addition, we develop an approach to GDM based on interval-valued hesitant preference relations in order to consider the differences of opinions between individual decision makers. Numerical examples are provided to illustrate the proposed approach.

Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis

Hesitant fuzzy sets (HFSs), which allow the membership degree of an element to a set represented by several possible values, can be considered as a powerful tool to express uncertain information in the process of group decision making. We derive some correlation coefficient formulas for HFSs and apply them to clustering analysis under hesitant fuzzy environments. Two real world examples, i.e. software evaluation and classification as well as the assessment of business failure risk, are employed to illustrate the actual need of the clustering algorithm based on HFSs, which can incorporate the difference of evaluation information provided by different experts in clustering processes. In order to extend the application domain of the clustering algorithm in the framework of HFSs, we develop the interval-valued HFSs and the corresponding correlation coefficient formulas, and then demonstrate their application in clustering with interval-valued hesitant fuzzy information through a specific numerical example.