IVIFS环境下基于模糊熵和得分函数的多准则决策方法

在区间直觉模糊环境和各准则的信息完全未知的条件下,本文提出了一个基于模糊熵和得分函数的多准则决策方法.基于区间直觉模糊集的准则形式,本文给出了模糊熵模型,从而可以确定各准则的权重.在决策方法方面,作者提出了区间直觉模糊集的加权记分函数和加权精确函数,解决了记分函数无法解决的加权问题的难题,同时给出了一种新的决策方法.最后,文章通过实例说明了该方法的可行性和有效性.     

关于IVIFS算子与IFS算子关系的讨论

Atanassov教授在给出区间教直觉模糊集算子的定义以后曾给出断语:直觉模糊集算子满足的结论,都可以推广到相应的区问数直党模糊集算子.本文详细分析多条直觉模糊集算子的性质与相应区间数直党模糊集算子的性质之间的关系,证明这些直觉模糊集算子的性质都不能推广到区间数直党模糊集算子,因而对Atanassov的论断进行了澄清.     

区间直觉模糊集的相似性测度及其应用

在度量两个集合时,用相似性测度来表示两集合的相似性程度.在度量区间直觉模糊集的相似性程度时,现有的很多方法都没有把犹豫度考虑在内.针对这个问题,根据区间直觉模糊集理论,在Szmidt的区间直觉模糊集的海明距离、规范化海明距离、欧几里得距离、规范化欧几里得距离的基础上.定义了基于Szmidt的区间直觉模糊集的加权海明距离和基于Szmidt的区间直觉模糊集的加权欧几里得距离,分别包含了隶属度,非隶属度和犹豫度,并给出了定理和证明.然后定义了两种区间直觉模糊集的相似性测度.最后将这两种相似性测度应用到模式识别领域.     

区间直觉正态模糊数集成算子及其在决策中的应用

研究了区间直觉正态模糊数(IVINFN)决策信息及其集成算子。首先,定义了区间直觉正态模糊数的概念,提出了运算法则;其次,给出了区间直觉正态模糊数诱导有序加权平均(IVINFN-IOWA)算子和区间直觉正态模糊数诱导有序加权几何(IVINFN-IOWGA)算子的概念,探讨了其性质;在此基础上,分别定义了基于均值和标准差的区间直觉正态模糊数的得分函数和精确函数,给出其排序方法。最后,针对属性值为区间直觉正态模糊数且权重已知的多属性决策问题,给出了其决策方法,并进行了实例分析,结果表明该决策方法是有效的。     

梯形直觉模糊数集成算子及在决策中的应用研究

在直觉模糊集理论基础上,用梯形模糊数表示直觉模糊数的隶属度和非隶属度,进而提出了梯形直觉模糊数;然后定义了梯形直觉模糊数的运算法则,给出了相应的证明,并基于这些法则,给出了梯形直觉模糊加权算数平均算子(TIFWAA)、梯形直觉模糊数的加权二次平均算子(TIFWQA)、梯形直觉模糊数的有序加权二次平均算子(TIFOWQA)、梯形直觉模糊数的混合加权二次平均算子(TIFHQA)并研究了这些算子的性质;建立了不确定语言变量与梯形直觉模糊数的转化关系,并证明了转化的合理性;定义了梯形直觉模糊数的得分函数和精确函数,给出了梯形直觉模糊数大小比较方法;最后提供了一种基于梯形直觉模糊信息的决策方法,并通过实例结果证明了该方法的有效性。     

基于区间直觉模糊熵和时间熵的动态多属性模糊决策

以熵理论为基础,针对属性权重和时间权重完全未知的动态多属性区间直觉模糊决策问题,首先针对现有区间直觉模糊熵公理化定义的缺陷进行了分析,提出一种改进的区间直觉模糊熵的公理化定义,并据此构造了区间直觉模糊熵的一个新的计算公式;其次,利用改进的区间直觉模糊熵确定属性权重;再次,基于时间度体现对近期数据的重视程度的基础上,利用时间权向量的信息熵为优化目标来确定时间权重;然后,利用区间直觉模糊几何加权算子进行集结,并利用区间直觉模糊集的排序函数对决策方案进行排序和择优。最后,通过一个实例分析,表明本文提出的方法的可行性和有效性,为动态多属性区间直觉模糊决策问题提供了一种新的方法和思路。     

区间直觉模糊集的模糊熵群决策方法

针对专家权重未知、专家判断信息以区间直觉模糊集给出的多属性群决策问题,提出了一种新的模糊熵决策方法。通过定义区间直觉模糊集的模糊熵判断专家信息的模糊程度,进而确定每位专家的权重;然后计算备选方案距理想方案和负理想方案的模糊交叉熵距离,得到每个专家对方案的排序;再分别利用加权算术算子和加权几何算子集结专家的排序结果,得到专家群体对方案的排序。实例分析验证了方法的有效性。     

区间直觉不确定语言几何Heronian平均算子及其在多属性群决策中的应用

针对决策信息为区间直觉不确定语言变量且属性间存在相互关联的多属性群决策问题,提出了一种基于区间直觉不确定语言几何加权Heronian平均算子的决策方法.首先对区间直觉不确定语言变量的概念、运算法则以及相关性质等做出界定,然后基于区间直觉不确定语言变量和Heronian平均算子,定义了新的区间直觉不确定语言几何Heronian平均算子和区间直觉不确定语言几何加权Heronian平均算子,并给出了基于IVIULN的MAGDM方法,最后通过实例验证了该算子的科学性与适用性.     

基于改进直觉模糊集成算子的多属性决策方法及其应用研究

基于直觉模糊集理论,提出了改进直觉模糊集成算子方法来研究多属性决策问题。本文定义了直觉模糊数的运算法则和比较了直觉模糊信息的一系列集成算子,然后改进了传统得分函数,并将其与直觉模糊集成算子相结合,从而得到新的直觉模糊信息的集成方式,将其运用于解决属性权重已知的直觉模糊多属性决策问题。最后,通过具体实例说明该方法的有效性和具体应用过程。     

基于二型直觉三角前景T2ITFNHA算子的多准则群决策方法

在二型直觉模糊集与直觉三角模糊数的基础上,定义了二型直觉三角模糊数及其运算法则,给出基于二型直觉三角模糊数的加权算术平均(WAA)算子,有序加权平均(OWA)算子和混合集结(HA)算子.考虑决策者有限理性决策行为下的异化风险态度与敏感性,定义二型直觉三角模糊前景效应与前景价值函数,构造前景T2ITFNHA算子.针对多方参与决策且决策者权重确定,准则权重未知的多准则群决策问题,采用正态分布赋权法计算前景T2ITFNHA算子指标置换下的位置权重,提出基于二型直觉三角前景T2ITFNHA算子的决策方法.该方法利用前景T2ITFNHA算子集结群体准则的二型直觉三角前景价值函数,运用灰色系统理论确定准则权重,并通过计算前景集对记分函数对方案进行对比和排序.最后,案例分析说明了二型直觉三角模糊数的实际应用背景及所提高的决策方法的有效性和可行性.     

基于区间粗糙直觉模糊数的多属性决策方法

研究了区间粗糙直觉模糊多属性决策。探讨了区间粗糙直觉模糊数的运算法则及其性质;定义了区间粗糙直觉模糊数的得分函数和精确函数,进而给出其排序方法;给出了区间粗糙直觉模糊数的变权算术平均和变权几何平均算子,并且建立了区间粗糙直觉模糊数的多属性决策模型;实例验证了所提出决策方法的有效性。     

Multicriteria Decision-making Method Based on Risk Attitude under Interval-valued Intuitionistic Fuzzy Environment

Based on the feature of interval-valued intuitionistic fuzzy multi-attribute decision-making, in this thesis, a mentality parameter is used to reflect the decision makers’ risk attitude in determining of both a membership degree and a non-membership degree. Besides, with the mentality parameter, a new score function and accuracy function are proposed, which integrate the membership degree, the non-membership degree and the hesitancy degree into one index. Furthermore, to compare two interval-valued intuitionistic fuzzy numbers, a new ranking method is generated with the score function and accuracy function. Finally, a multi-attribute decision method under interval-valued intuitionistic fuzzy environment is developed in a linear weighted average operator. And promising numerical results show that this method is available.     

Continuous interval-valued intuitionistic fuzzy aggregation operators and their applications to group decision making

In this paper, we introduce a new operator called the continuous interval-valued intuitionistic fuzzy ordered weighted averaging (C-IVIFOWA) operator for aggregating the interval-valued intuitionistic fuzzy values. It combines the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator and the continuous ordered weighted averaging (C-OWA) operator by a controlling parameter, which can be employed to diminish fuzziness and improve the accuracy of decision making. We further apply the C-IVIFOWA operator to the aggregation of multiple interval-valued intuitionistic fuzzy values and obtain a wide range of aggregation operators including the weighted C-IVIFOWA (WC-IVIFOWA) operator, the ordered weighted (OWC-IVIFOWA) operator and the combined C-IVIFOWA (CC-IVIFOWA) operator. Some desirable properties of these operators are investigated. And finally, we give a numerical example to illustrate the applications of these operators to group decision making under interval-valued intuitionistic fuzzy environment.     

区间直觉梯形模糊Bonferroni平均算子及在多属性群决策中的应用

针对决策信息为区间直觉梯形模糊数(IVITFN)且属性间存在相互关联的多属性群决策(MAGDM)问题,提出一种基于加权区间直觉梯形模糊Bonferroni平均(WIVITFBM)算子的决策方法.首先,基于IVITFN的运算法则和Bonferroni平均(BM)算子,定义了区间直觉梯形模糊Bonferroni平均(VITFBM)算子和WIVITFBM算子.然后,研究了这些算子的一些性质,建立基于WIVITFBM算子的MAGDM模型,结合排序方法进行决策。最后通过MAGDM算例验证了该算子的有效性与可行性。     

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.     

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.     

基于新精确函数的区间直觉模糊多属性决策方法

基于区间直觉模糊数隶属度和非隶属度构成的二维几何图形特征给出区间直觉模糊数精确函数的新定义,并将其作为区间直觉模糊数的排序指标,区间直觉模糊数的精确函数值越大,则区间直觉模糊数就越大,进而提出一种权重信息不完全确定的区间直觉模糊多属性决策方法.通过算例分析说明所提出排序指标的有效性和决策方法的可行性.     

Some geometric aggregation operators based on interval intuitionistic uncertain linguistic variables and their application to group decision making

With respect to multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights take the form of crisp numbers, and attribute values take the form of interval-valued intuitionistic uncertain linguistic variables, some new group decision making analysis methods are developed. Firstly, some operational laws, expected value and accuracy function of interval-valued intuitionistic uncertain linguistic variables are introduced. Then, an interval-valued intuitionistic uncertain linguistic weighted geometric average (IVIULWGA) operator and an interval-valued intuitionistic uncertain linguistic ordered weighted geometric (IVIULOWG) operator have been developed. Furthermore, some desirable properties of the IVIULWGA operator and the IVIULOWG operator, such as commutativity, idempotency and monotonicity, have been studied, and an interval-valued intuitionistic uncertain linguistic hybrid geometric (IVIULHG) operator which generalizes both the IVIULWGA operator and the IVIULOWG operator, was developed. Based on these operators, an approach to multiple attribute group decision making with interval-valued intuitionistic uncertain linguistic information has been proposed. Finally, an illustrative example is given to verify the developed approaches and to demonstrate their practicality and effectiveness.     

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。