Fuzzy Multiple Attribute Group Decision Research Articles

Extended Heronian Mean Based on Hesitant Fuzzy Linguistic Information for Multiple Attribute Group Decision‐Making

This paper investigates the hesitant fuzzy linguistic multiple attribute group decision‐making (MAGDM) problem with the heterogeneous relationship among the attribute variables that cannot be solved by most existing decision‐making methods. To address this problem, a new operator is introduced based on partitioning attribute variables into different sets according to their interrelationship. This operator is called the extended Heronian mean (EHM) operator. To obtain each expert’s comprehensive values of the alternatives in the hesitant fuzzy linguistic MAGDM problem, we investigate the EHM operator under a hesitant fuzzy linguistic environment and propose the hesitant fuzzy linguistic EHM operator and the hesitant fuzzy linguistic linear support degree weighted EHM operator. In addition, the axiom definition of a linguistic type similarity measure of hesitant fuzzy linguistic term sets is proposed. The weight of the experts can be determined based on this type similarity measure. Finally, a practical case is presented to demonstrate the steps of our method, and a comparison analysis illustrates its feasibility and effectiveness.

An Extended Bidirectional Projection Method for Picture Fuzzy MAGDM and Its Application to Safety Assessment of Construction Project

In this article, we shall introduce picture fuzzy bidirectional projection method and some fundamental theories of picture fuzzy information. First of all, we briefly review the definition of picture fuzzy sets (PFS) and introduce the score function, accuracy function and operational laws of PFS. In addition, to fuse overall picture fuzzy evaluation information, we introduce two aggregation operators of PFS including PFWA and PFWG operators. Furthermore, the picture fuzzy bidirectional projection method for multiple attribute group decision making (MAGDM) is established and all computing steps are simply depicted. In our presented model, it’s more accuracy and effective for considering the conflicting attributes. Finally, a numerical example for safety assessment of construction project has been given to illustrate this new model and some comparisons between bidirectional projection method with picture fuzzy numbers (PFNs) and PFWA, PFWG aggregation operators are also conducted to further illustrate advantages of the new method.

Hesitant Probabilistic Fuzzy Information Aggregation Using Einstein Operations

In this paper, a hesitant probabilistic fuzzy multiple attribute group decision making is studied. First, some Einstein operations on hesitant probability fuzzy elements such as the Einstein sum, Einstein product, and Einstein scalar multiplication are presented and their properties are discussed. Then, several hesitant probabilistic fuzzy Einstein aggregation operators, including the hesitant probabilistic fuzzy Einstein weighted averaging operator and the hesitant probabilistic fuzzy Einstein weighted geometric operator and so on, are introduced. Moreover, some desirable properties and special cases are investigated. It is shown that some existing hesitant fuzzy aggregation operators and hesitant probabilistic fuzzy aggregation operators are special cases of the proposed operators. Further, based on the proposed operators, a new approach of hesitant probabilistic fuzzy multiple attribute decision making is developed. Finally, a practical example is provided to illustrate the developed approach.

Gray Method for Multiple Attribute Decision Making with Incomplete Weight Information under the Pythagorean Fuzzy Setting

Abstract In this study, we developed an approach to investigate multiple attribute group decision-making (MAGDM) problems, in which the attribute values take the form of Pythagorean fuzzy numbers whose information about attribute weights is incompletely known. First, the Pythagorean fuzzy Choquet integral geometric operator is utilized to aggregate the given decision information to obtain the overall preference value of each alternative by experts. In order to obtain the weight vector of the criteria, an optimization model based on the basic ideal of the traditional gray relational analysis method is established, and the calculation steps for solving Pythagorean fuzzy MAGDM problems with incompletely known weight information are given. The degree of gray relation between every alternative and positive-ideal solution and negative-ideal solution is calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of gray relation to both the positive-ideal solution and negative-ideal solution simultaneously. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Autocratic decision making using group recommendations based on hesitant fuzzy sets for green hotels selection and bidders selection

In this paper, we propose a new method for autocratic decision making using group recommendations (ADMUGRs) based on hesitant fuzzy sets (HFSs), where hesitant fuzzy decision matrices (HFDMs) provided by decision makers (DMs) are represented by hesitant fuzzy elements (HFEs). The proposed method is simpler than He et al.’s method (2015) due to the fact that He et al.’s method uses the complicated hesitant fuzzy power Bonferroni mean operator and the complicated hesitant fuzzy power geometric Bonferroni mean operator for hesitant fuzzy multiple attribute group decision making. We also use the proposed method for dealing with the “green hotels selection problem” and the “bidders selection problem”. The proposed method for ADMUGRs based on HFSs provides us with a very useful way for autocratic decision making using group recommendations in hesitant fuzzy environments.

Interval-valued Pythagorean fuzzy GRA method for multiple-attribute decision making with incomplete weight information

In this paper, the concept of multiple-attribute group decision-making (MAGDM) problems with interval-valued Pythagorean fuzzy information is developed, in which the attribute values are interval-valued Pythagorean fuzzy numbers and the information about the attribute weight is incomplete. Since the concept of interval-valued Pythagorean fuzzy sets is the generalization of interval-valued intuitionistic fuzzy set. Thus, due the this motivation in this paper, the concept of interval-valued Pythagorean fuzzy Choquet integral average (IVPFCIA) operator is introduced by generalizing the concept of interval-valued intuitionistic fuzzy Choquet integral average operator. To illustrate the developed operator, a numerical example is also investigated. Extended the concept of traditional GRA method, a new extension of GRA method based on interval-valued Pythagorean fuzzy information is introduced. First, utilize IVPFCIA operator to aggregate all the interval-valued Pythagorean fuzzy decision matrices. Then, an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method is established, to get the weight vector of the attributes. Based on the traditional GRA method, calculation steps for solving interval-valued Pythagorean fuzzy MAGDM problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution is calculated. To determine the ranking order of all alternatives, a relative relational degree is defined by calculating the degree of grey relation to both the positive-ideal solution and negative ideal solution simultaneously. Finally, to illustrate the developed approach a numerical example is to demonstrate its practicality and effectiveness.

A novel aggregation method for Pythagorean fuzzy multiple attribute group decision making

A novel aggregation method for Pythagorean fuzzy multiple attribute group decision making

The expert reliability and evidential reasoning rule based intuitionistic fuzzy multiple attribute group decision making

The expert reliability and evidential reasoning rule based intuitionistic fuzzy multiple attribute group decision making

Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power Heronian aggregation operators

The power average operator can relieve the some influences of unreasonable data given by biased decision makers, and Heronian mean operator can consider the interrelationship of the aggregated arguments. In order to take full advantages of these two kinds of operators, in this paper, we combined the power average operator with Heronian mean operator and extended them to process interval-valued intuitionistic fuzzy information, and proposed the interval-valued intuitionistic fuzzy power Heronian aggregation (IVIFPHA) operator, interval-valued intuitionistic fuzzy power weight Heronian aggregation (IVIFPWHA) operator. At the same time, we presented a new similarity function of IVIFNs as support degree in power weighting, and it has a good reliability and accuracy. Further, some properties of these new aggregation operators are investigated and some special cases are discussed, and a new technique based on these operators for fuzzy multiple attribute group decision making (MAGDM) was presented. Finally, an illustrative example was given to illustrate the effectiveness and advantages of the developed method by comparing with the existing methods.

The evaluation study on tourism websites: from the perspective of triangular intuitionistic fuzzy multiple attribute group decision making

ABSTRACTThe research on the evaluation of tourism websites can measure the websites' development in the same industry, and it is helpful to improve the quality of the websites. In view of the complexity of the tourism website evaluation, this study firstly constructs the evaluation index system of tourism websites, including subjective indicators and objective indicators. Six websites are taken for example from non-profit tourism websites, direct tourism websites and intermediary tourism websites, two from each type. And then the evaluation index data is obtained; triangular fuzzy number and subjective preference value of evaluation index are calculated; fuzzy evaluation matrix, normalization matrix and the expectation matrix are constructed; the index weight is determined; tourism website comprehensive expectation is calculated and the website ranking according to comprehensive expectation is given from high to low.

Contrasting Correlation Coefficient with Distance Measure in Interval Valued Intuitionistic Trapezoidal Fuzzy MAGDM Problems

In this paper, Multiple Attribute Group Decision Making (MAGDM) problems in which the data is of the form of Interval Valued Intuitionistic Trapezoidal Fuzzy Numbers (IVITzFNs) is presented. Some operational laws of IVITzFNs are introduced. Then some new aggregation operators including interval valued Intuitionistic Trapezoidal Fuzzy Weighted Averaging (IVITzFWA) operator, interval valued Intuitionistic Trapezoidal Fuzzy Ordered Weighted Averaging (IVITzFOWA) operator and Interval Intuitionistic Trapezoidal Fuzzy Hybrid Averaging (IVITzFHA) operator, are proposed and some desirable properties of these operators are studied, such as Commutativity, Idempotency and Monotonicity. A new distance function and correlation coefficient are proposed for IVITzFNs which will be utilized for ranking the alternatives in MAGDM problems. Finally, numerical illustrations are given to verify the developed approach and to demonstrate its practicality and effectiveness.

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.

Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems

Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations are often required to handle uncertainty, imprecise and inadequate information, which are well suited to represent with fuzzy values. This paper develops a VIKOR method based on intuitionistic fuzzy sets with multi-judges and multi-attributes in real-life situations. Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate individual judgments of experts to rate the importance of attributes and alternatives. Then, an intuitionistic ranking index is introduced to obtain a compromise solution to solve MAGDM problems. For application and validation, this paper presents two application examples and solves the practical portfolio selection and material handling selection problems to verify the proposed method. Finally, the intuitionistic fuzzy VIKOR method is compared with the existing intuitionistic fuzzy MAGDM method for two application examples, and their computational results are discussed.

A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method

Supplier selection is a key issue in supply chain management, which directly impacts the manufacturer's performance. The problem can be viewed as a multiple attribute group decision making (MAGDM) that concerns many conflicting evaluation attributes, both being of qualitative and quantitative nature. Due to the increasing complexity and uncertainty of socio-economic environment, some evaluations of attributes are not adequately represented by numerical assessments and type-1 fuzzy sets. In this paper, we develop some linear programming models with the aid of multidimensional analysis of preference (LINMAP) method to solve interval type-2 fuzzy MAGDM problems, in which the information about attribute weights is incompletely known, and all pairwise comparison judgments over alternatives are represented by IT2FSs. First, we introduce a new distance measure based on the centroid interval between the IT2FSs. Then, we construct the linear programming model to determine the interval type-2 fuzzy positive ideal solution (IT2PIS) and corresponding attributes weight vector. Based on it, an extended LINMAP method to solve MAGDM problem under IT2FSs environment is developed. Finally, a supplier selection example is provided to demonstrate the usefulness of the proposed method.

Software change decision support: a proposed framework and case study

Software change is a common and continuous process. It must be controlled in a rigorous way to avoid software quality degradation. Despite the existence of a number of research studies on software change control, software professionals often rely on their experience and intuition to decide whether to accept or reject software change requests. The research reported on here suggests a framework for software change decision support. The first part of the framework is a conceptual model which contains a comprehensive list of software attributes that a software change might affect. The literature has been surveyed in order to construct this conceptual model. The second part of the framework provides a formulation of the software change decision as a fuzzy multiple attribute group decision problem. Software analysts are required to linguistically assess the impact of every change implementation solution on every attribute. Then, the provided solution algorithm for the fuzzy multiple attribute decision problem has to be executed in order to come out with a ranking of the different implementation solutions. An implementation of the framework has been validated through a limited industrial application. This experiment proved that the proposed solution has an interesting potential.

Multiple attribute group decision making based on generalized power aggregation operators under interval-valued dual hesitant fuzzy linguistic environment

This paper aims to investigate the type of fuzzy multiple attribute group decision making (MAGDM) where arguments being aggregated are allowed to support each other. In order to enable decision makers to express their preferences more comprehensively, we firstly put forward a hybrid tool, an interval-valued dual hesitant fuzzy linguistic set (IVDHFLS), which employs interval-valued hesitant membership and nonmembership degrees to assess linguistic terms. Basic operational laws for IVDHFLS are discussed, also a distance measure is designed to overcome irrationality in traditional methodology for hesitant fuzzy sets, i.e., artificially adding values to mismatching membership or nonmembership degrees. We next develop fundamental generalized power average aggregation operators for IVDHFLS, including power average operator, power geometric average operator, power ordered weighted average operator and power ordered weighted geometric average operator. Desirable properties and special cases of these aggregation operators are further analyzed. Furthermore, based on the generalized operators above, we construct two approaches for MAGDM with mutually supportive arguments being aggregated under interval-valued dual hesitant fuzzy linguistic environments. Finally, case studies are conducted to verify effectiveness and practicality of the developed approaches.

Hesitant Fuzzy Power Bonferroni Means and Their Application to Multiple Attribute Decision Making

As a useful generalization of fuzzy sets, the hesitant fuzzy set is designed for situations in which it is difficult to determine the membership of an element to a set because of ambiguity between a few different values. In this paper, we define the i th-order polymerization degree function and propose a new ranking method to further compare different hesitant fuzzy sets. In order to obtain much more information in the process of group decision making, we combine the power average operator with the Bonferroni mean in hesitant fuzzy environments and develop the hesitant fuzzy power Bonferroni mean and the hesitant fuzzy power geometric Bonferroni mean. We investigate the desirable properties of these new hesitant fuzzy aggregation operators and discuss some special cases. The new aggregation operators are utilized to present techniques for hesitant fuzzy multiple attribute group decision making. Finally, a numerical example is provided to illustrate the effectiveness of the developed techniques.

A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information

This paper develops a new method for solving multiple attribute group decision-making (MAGDM) problems with Atanassov’s interval-valued intuitionistic fuzzy values (AIVIFVs) and incomplete attribute weight information. Firstly, we investigate the asymptotic property of the Atanassov’s interval-valued intuitionistic fuzzy (AIVIF) matrix. It is demonstrated that after applying weights an infinite number of times, all elements in an AIVIF matrix will approach the same AIVIFV without regard to the initial values of elements. Then, the weight of each decision maker (DM) with respect to every attribute is determined by considering the similarity degree and proximity degree simultaneously. To avoid weighting an AIVIF matrix too many times, the collective decision matrix is transformed into an interval matrix using the risk coefficient of DMs. Subsequently, to derive the attribute weights objectively, we construct a multi-objective interval-programming model that is solved by transforming it into a linear program. The ranking order of alternatives is generated by the comprehensive interval values of alternatives. Finally, an example of a research and development (R&D) project selection problem is provided to illustrate the implementation process and applicability of the method developed in this paper.

Multiple attribute group decision making in interval type-2 fuzzy environment using a new distance formulation

Type-2 fuzzy sets are generalisations of ordinary fuzzy sets in which the membership grades are characterised by fuzzy membership functions. Here, we consider the problem of finding the distance between two interval type-2 fuzzy sets (IT2-FSs). We define the formulation of calculating the distance between two IT2-FSs based on a new definition of centroid for an IT2-FS and explain an algorithm for obtaining it. Then, based on our proposed distance formulation, we present a method to deal with fuzzy multiple attributes group decision-making (FMAGDM) problems. We also use two examples to illustrate the FMAGDM process of the proposed method. The method provides us with a useful way to deal with FMAGDM problems based on IT2-FSs.

Fuzzy Multiple Attributes Group Decision-Making Based on Ranking Interval Type-2 Fuzzy Sets and the TOPSIS Method

In this paper, we present a new method for fuzzy multiple attributes group decision-making based on the proposed ranking method of trapezoidal interval type-2 fuzzy sets and the TOPSIS method. Firstly, we present a new method for ranking trapezoidal interval type-2 fuzzy sets. Secondly, we construct the decision matrix and the average decision matrix, respectively. Thirdly, we construct the weighting decision matrix and average weighting decision matrix, respectively. Fourthly, we construct the ranking decision matrix by calculating the ranking values of trapezoidal interval type-2 fuzzy sets in the average decision matrix. Then, we get the absolute positive ideal solution and the absolute negative ideal solution with respect to the attributes based on the ranking decision matrix. Then, we calculate the distance between each alternative and the absolute positive ideal solution and calculate the distance between each alternative and the negative ideal solution, respectively. Finally, we calculate the relative degree of closeness of each alternative. The larger the value of the relative degree of closeness of an alternative, the better the preference order of the alternative. The proposed fuzzy multiple attributes group decision-making method can overcome the drawbacks of the existing method.