基于TOPSIS的属性关联直觉模糊多属性决策方法

针对属性值为直觉模糊数,已知部分属性偏好关系及属性交互类型的属性关联多属性决策问题给出决策方法.首先定义方案到正(负)理想方案的距离及各方案与正理想方案相对贴近度.然后以极大化各方案与相对贴近度为目标建立优化模型,确定出属性集的模糊测度.进而基于直觉模糊Choquet积分算子计算各方案的直觉模糊综合评价值,再根据直觉模糊数的得分值及精确度得到方案的排序.最后通过实例验证了方法的有效可行性.     

模糊多属性决策的直觉模糊集方法

基于直觉模糊集理论,提出了一种新的TOPSIS方法来研究模糊多属性决策问题。首先,根据直觉模糊集的几何意义,定义了两个直觉模糊集之间的距离,且每个备选方案的评价值用直觉模糊值表示;然后,根据TOPSIS原理,通过计算备选方案到直觉模糊正理想解和负理想解的距离,来确定备选方案的综合评价指数,以此判断方案的优劣次序。最后,通过一个具体实例说明该方法的有效性和具体应用过程。     

基于区间值直觉模糊集的TOPSIS多属性决策

基于区间值直觉模糊集,提出了一种新的TOPSIS模糊多属性决策方法。首先介绍区间直觉模糊集的概念,定义了两个区间值直觉模糊集之间的距离;然后根据TOPSIS方法的原理,定义了两个区间值直觉模糊集的接近系数,通过计算备选方案到区间值直觉模糊正理想解和负理想解的距离来确定接近系数,从而判断备选方案的优劣次序。最后,通过一个具体实例来说明这种方法的有效性和具体计算过程。     

基于相关系数及改进TOPSIS的区间直觉模糊群决策方法

针对决策者权重与属性权重完全未知的区间直觉模糊多属性群决策问题,给出一种基于相关系数及改进TOPSIS法的多属性群决策方法。将各决策者同等对待,得到各方案关于每个属性的评价均值,由各决策者在每个方案下关于单个属性的区间直觉模糊评价值与其评价均值的相关系数,获取在单个属性下体现出的各决策者权重。基于各决策者权重得到群体区间直觉模糊决策矩阵,构建各方案与正理想方案加权相关系数总和最大化(或与负理性方案加权相关系数总和最小化)的目标规划模型确定各属性权重。以两组属性权重向量分别得到各方案与正、负理想方案的加权相关系数,依据改进的TOPSIS法计算各方案与正理想方案的相对相关系数,并以此得到各方案的优先序。投资项目选择算例说明该群决策方法有效性与合理性。     

基于模糊熵的直觉模糊多属性群决策方法

针对专家权重未知、专家判断信息以直觉模糊集给出的多属性群决策问题,提出了一种新的决策方法.通过定义直觉模糊集的模糊熵计算专家判断信息的模糊程度,进而确定每位专家的权重.然后定义直觉模糊集的模糊交叉熵确定备选方案距理想方案和负理想方案的距离,再根据加权算术算子集结专家的判断信息,得到方案的排序.最后,通过一个实例分析验证了方法的有效性.     

基于直觉模糊软信息的多属性群决策方法

针对属性权重和专家权重信息都完全未知的多属性群决策问题,提出了一类以直觉模糊软集为数据环境的群决策方法。通过提取理想点结合距离测度构建非线性规划模型来求解属性权重。利用得分函数进行矩阵变换,基于各对象的综合正、负理想值构造满意度,并根据总体满意度最大化原则构建规划模型确定专家权重。最后利用属性权重和专家权重对得分矩阵进行加权平均,计算各对象的综合得分,进而给出具体的多属性群决策过程,并实例验证了决策方法的可行性和合理性。     

基于投影模型的区间直觉模糊多属性决策方法研究

本文首先定义了一种新区间直觉模糊投影方法,其能更好地度量投影向量之间的相关性。其次,根据区间直觉模糊正负理想方案与备选方案的投影关系, 构建了基于投影方法的未知属性权重求解模型,并在此基础上设计了一种基于投影值的贴近度方法,能实现对备选方案有效排序;最后,用实例验证了该方法的有效性和可行性。     

基于Hausdauff度量的模糊TOPSIS方法研究

针对模糊多属性决策中的模糊 TOPSIS方法 ,提出了一种基于 Hausdauff度量的模糊 TOPSIS方法 .首先由模糊极大集与模糊极小集确定模糊多属性决策问题的理想解与负理想解 ,进而由 Hausdauff度量获得不同备选方案到理想解与负理想解的距离及其贴近度 ,根据贴近度指标对方案进行排序 ,为决策者提供决策支持 .最后以 L-R梯形模糊数为例进行了实例研究 .     

基于TOPSIS的区间直觉模糊多属性决策法

对基于区间直觉模糊信息的多属性决策问题进行了研究。给出了区间直觉模糊数之间的距离公式,并定义了区间直觉模糊正、负理想点,进而提出了一种基于TOPSIS的区间直觉模糊多属性决策方法。最后进行了实例分析。     

基于直觉模糊相似度量的群决策专家水平评判方法

针对基于直觉模糊信息的多属性群决策专家水平评判问题提出了理想矩阵分析法.在引入多属性群决策直觉模糊信息体(即决策信息体)和直觉模糊相似度量的基础上,通过计算决策矩阵与正、负理想矩阵之间的相似度,提出了基于直觉模糊相似度量的理想矩阵分析法,并利用该方法对算例中的专家评判水平进行排序,通过比较统计分析法和直觉模糊熵分析法说明该方法的可行性和有效性.     

Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment

TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy decision matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.     

Extension of the VIKOR method for group decision making with interval-valued intuitionistic fuzzy information

The aim of this paper is to extend the VIKOR method for multiple attribute group decision making in interval-valued intuitionistic fuzzy environment, in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number, and the information about attribute weights is partially known, which is an important research field in decision science and operation research. First, we use the interval-valued intuitionistic fuzzy hybrid geometric operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. We use the different distances to calculate the particular measure of closeness of each alternative to the interval-valued intuitionistic positive-ideal solution. According to values of the particular measure, we rank the alternatives and then select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.     

一种基于Choquet积分的多属性直觉模糊决策方法

研究了决策者对方案的主观偏好值以及属性值均为直觉模糊数的且属性间存在关联的多属性决策问题.利用Choquet模糊积分作为集结算子,构建了基于属性关联的M OD和SOD模型.通过求解模型获得属性的权重,进而给出了一种新的直觉模糊多属性决策方法.最后通过一个算例说明了该决策方法的有效性和可行性.     

Multi-person multi-attribute decision making models under intuitionistic fuzzy environment

Intuitionistic fuzzy numbers, each of which is characterized by the degree of membership and the degree of non-membership of an element, are a very useful means to depict the decision information in the process of decision making. In this article, we investigate the group decision making problems in which all the information provided by the decision makers is expressed as intuitionistic fuzzy decision matrices where each of the elements is characterized by intuitionistic fuzzy number, and the information about attribute weights is partially known, which may be constructed by various forms. We first use the intuitionistic fuzzy hybrid geometric (IFHG) operator to aggregate all individual intuitionistic fuzzy decision matrices provided by the decision makers into the collective intuitionistic fuzzy decision matrix, then we utilize the score function to calculate the score of each attribute value and construct the score matrix of the collective intuitionistic fuzzy decision matrix. Based on the score matrix and the given attribute weight information, we establish some optimization models to determine the weights of attributes. Furthermore, we utilize the obtained attribute weights and the intuitionistic fuzzy weighted geometric (IFWG) operator to fuse the intuitionistic fuzzy information in the collective intuitionistic fuzzy decision matrix to get the overall intuitionistic fuzzy values of alternatives by which the ranking of all the given alternatives can be found. Finally, we give an illustrative example.     

Correlation coefficient of interval-valued intuitionistic fuzzy sets and its application to multiple attribute group decision making problems

In this paper, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFN), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then we use the obtained attribute weights and the interval-valued intuitionistic fuzzy weighted geometric (IIFWG) operator to fuse the interval-valued intuitionistic fuzzy information in the collective interval-valued intuitionistic fuzzy decision matrix to get the overall interval-valued intuitionistic fuzzy values of alternatives, and then rank the alternatives according to the correlation coefficients between IVIFNs and select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.     

A group decision making approach based on aggregating interval data into interval-valued intuitionistic fuzzy information

Group decision making is one of the most important problems in decision making sciences. The aim of this article is to aggregate the interval data into the interval-valued intuitionistic fuzzy information for multiple attribute group decision making. In this model, the decision information is provided by decision maker, which is characterized by interval data. Based on the idea of mean and variance in statistics, we first define the concepts of satisfactory and dissatisfactory intervals of attribute vector against each alternative. Using these concepts, we develop an approach to aggregate the attribute vector into interval-valued intuitionistic fuzzy number under group decision making environment. A practical example is provided to illustrate the proposed method. To show the validity of the reported method, comparisons with other methods are also made.     

直觉模糊多属性的意见集中排序法

研究了属性权重信息不完全确定,属性值为直觉模糊集的多属性决策问题。首先根据直觉模糊数的得分函数和精确函数对决策矩阵中的评价值比较大小,进而按属性集中的每个属性对方案排成线性序;然后通过计算赋权模糊优先矩阵确定方案的优属度,建立规划模型确定属性的权重;再利用加权算术算子对方案集结,得到专家对方案的排序,从而得到一种新的意见集中排序的决策方法。数值实例说明该方法的有效性和实用性,可为解决直觉模糊多属性决策提供新方法     

基于新模糊熵和得分函数的区间直觉模糊多属性决策

属性权重的确定以及对区间直觉模糊数的排序是多属性决策问题中两个最为关键的点。本文主要针对属性权重完全未知的多属性决策问题进行了研究,分析了现有大多数研究中关于区间直觉模糊熵和得分函数存在的局限性,进而提出了一种将不确定度和犹豫度相结合的新的模糊熵和得分函数。最后,通过对比实验证实了本文所提出的熵和得分函数应用到多属性决策中的有效性和合理性。