一种基于直觉模糊集相似度的多属性决策方法

直觉模糊集（ＩＦＳ）是对模糊集理论的一种扩充,能更好地处理模糊概念．首先给出一种新的直觉模糊集相似度;然后提出基于直觉模糊集相似度的多属性决策方法;最后通过线性目标规划模型和直觉模糊集相似度,得到属性的最优权重和相应的方案排序．数值实例表明,该方法是有效而可行的．     

考虑未知属性权重的区间直觉模糊VIKOR方法

针对传统模糊集方法处理不确定性多属性决策问题时只考虑隶属度信息的缺点,提出了基于区间直觉模糊集的VIKOR决策方法。区间直觉模糊集用来处理区间语义评价信息。考虑属性权重未知的问题,基于区间直觉模糊数间的支持度确定属性权重,属性的支持度越高,则其权重越小。将区间直觉模糊交叉熵引入区间直觉模糊VIKOR方法用于计算区间直觉模糊数间的距离。最后以某手机设计方案评价为例,验证了所提方法的有效性。     

基于多粒度粗糙直觉犹豫模糊集的最优粒度选择方法

为了对含有多属性的直觉犹豫模糊决策信息系统进行约简,获取最优粒度,运用多粒度粗糙集处理直觉犹豫模糊决策信息系统中的不确定信息,并对多粒度粗糙直觉犹豫模糊集的最优粒度选择方法进行了研究.首先,在直觉犹豫模糊集的基础上引入属性信息,给出粗糙直觉犹豫模糊集的概念,提出乐观、悲观多粒度粗糙直觉犹豫模糊集的下、上近似这4种模型,且研讨了它们的性质.其次,主要定义了基于悲观多粒度粗糙直觉犹豫模糊集下近似的粒度质量相似度和内、外粒度重要度的计算公式,设计了其最优粒度选择算法.最后,通过葡萄酒测评的案例,分别基于乐观、悲观多粒度粗糙直觉犹豫模糊集的下、上近似这4种情况,计算出最优粒度并进行了分析,验证了该算法在直觉犹豫模糊决策信息系统中的约简是有效的.     

基于熵和相关系数的直觉模糊多属性决策方法

针对决策信息为直觉模糊信息且属性权重完全未知的多属性决策问题,提出了一种基于直觉模糊熵和得分函数的决策方法。为了准确度量直觉模糊集的直觉性和模糊性,首先提出了一种新的直觉模糊熵,并讨论其相关性质。其次为了减少不确定信息对决策的影响,结合直觉模糊熵建立规划模型,从而确定属性权重。 同时从隶属度,非隶属度和犹豫度三方面构建论域对象与理想对象之间相关系数,并在此基础上根据决策者的决策态度定义得分函数进而得到最优决策。 最后给出一种基于直觉模糊信息的多属性决策方法,并通过候选人评估实例验证了该方法的可行性和有效性。     

一种基于直觉模糊集相似度的多属性决策方法

直觉模糊集（ＩＦＳ）是对模糊集理论的一种扩充,能更好地处理模糊概念．首先给出一种新的直觉模糊集相似度;然后提出基于直觉模糊集相似度的多属性决策方法;最后通过线性目标规划模型和直觉模糊集相似度,得到属性的最优权重和相应的方案排序．数值实例表明,该方法是有效而可行的．

     

基于熵和改进的协相关度的直觉模糊决策方法

针对决策信息为直觉模糊集且属性权重未知的多属性决策问题,以及关于协相关度的决策方法研究中存在的问题,提出了一种基于直觉模糊熵和改进的协相关度的决策方法。为准确度量直觉模糊集的直觉性和模糊性,给出了一种改进的直觉模糊熵的公式,概括并推广了原有的一类直觉模糊熵的公式,并讨论了其相关性质。然后由概率统计中相关系数的构造思想,改进了直觉模糊集协相关度的定义,构造了直觉模糊集与理想对象之间的相关系数以及得分函数,由此给出了一种改进的基于直觉模糊信息的多属性决策方法,最后通过实例计算验证了该方法的有效性和可行性。     

基于直觉模糊证据合成的多传感器目标识别

对证据理论和直觉模糊集理论之间的本质联系进行分析,提出一种基于直觉模糊集改进的证据合成实用算法用于多传感器目标识别.根据直觉模糊集中隶属度和非隶属度的概念,对证据理论的可信度函数模型进行改进,提出了直觉模糊可信度分配函数模型并构造了相应的证据合成规则,以提高证据合成计算效率,使合成结果便于最终决策.通过与其他算法的对比实验验证了所提出算法的有效性.     

基于直觉模糊粗糙集相似度的多属性决策方法

将直觉模糊粗糙集应用于多属性决策问题,提出了基于改进的直觉模糊粗糙集相似度的多属性决策方法。针对现有的直觉模糊粗糙集相似度忽略犹豫度而造成度量不精确的问题,提出了一种改进的直觉模糊粗糙集相似性度量方法,并揭示其若干重要性质。在此基础上,将属性值用直觉模糊粗糙集表示,并通过各个方案与直觉模糊粗糙集正、负理想方案的相似度比较,实现决策方案排序。数值实例表明了该方法的可行性和有效性,其在态势评估、目标识别等信息融合领域有良好的应用前景。     

基于Zhenyuan积分的直觉模糊多属性决策方法

针对属性之间具有相互关联关系的直觉模糊多属性决策问题,提出一种基于Zhenyuan积分的决策方法.首先提出直觉模糊Zhenyuan积分平均(IFZA)算子;然后探讨IFZA算子的优良性质以及与现有直觉模糊集成算子的关系,研究表明,IFZA算子可以改进现有直觉模糊奇异积分算子的缺陷,能够全面度量属性之间的相互关联关系;最后提出一种基于IFZA算子的属性间具有相互关联关系的直觉模糊多属性决策方法,并通过实例验证所提出方法的有效性和可行性.     

直觉模糊多属性决策方法综述

直觉模糊多属性决策是当前决策领域的一个研究热点,在实际决策中有着广泛的应用.按照直觉模糊集的发展形式:直觉模糊集、区间直觉模糊集、直觉三角模糊数和直觉梯形模糊数,分别介绍它们在多属性决策与群决策中的研究现状,并对其未来的发展方向进行了探讨与展望.     

基于直觉模糊推理的多属性群决策方法研究*

针对属性取值以直觉模糊数形式给出的多属性决策问题,提出了基于直觉模糊推理的多属性群决策方法。首先针对专家的评价信息构建直觉决策推理规则,然后根据规则之间的关系给出了决策推理模型,进而给出了基于直觉模糊推理的决策方法;最后通过购房实例验证了该方法的正确性和有效性。     

Multiple attribute group decision-making methods with unknown weights in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting

To better solve the corresponding multiple attribute group decision-making problem with unknown weights, multiple attribute group decision-making methods with completely unknown weights of decision-makers and incompletely known weights of attributes are proposed in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting. In the group decision-making method, two weight models are proposed based on the score function to determine the weights of both experts and attributes from the intuitionistic fuzzy decision matrices and the interval-valued intuitionistic fuzzy decision matrices. Then, overall evaluation formulas of weighted scores for each alternative are introduced in the intuitionistic fuzzy setting and the interval-valued intuitionistic fuzzy setting to obtain the ranking order of alternatives and the most desirable one(s). Finally, two numerical examples demonstrate the applicability and benefit of the proposed methods.     

基于改进熵和新得分函数的区间直觉模糊多属性决策

针对决策信息为区间直觉模糊数且属性权重完全未知的多属性决策问题, 提出基于改进的区间直觉模糊熵和新得分函数的决策方法. 首先, 利用改进的区间直觉模糊熵确定属性权重; 然后, 利用区间直觉模糊加权算术平均算子集成信息, 得到各备选方案的综合属性值, 进而指出现有得分函数存在排序失效或排序不符合实际的不足, 同时给出一个新的得分函数, 并以此对方案进行排序; 最后, 通过实例表明了所提出方法的有效性.

     

直觉模糊逻辑算子研究’

模糊逻辑算子是模糊信息融合、模糊推理及模糊决策的重要工具。针对作为模糊逻辑算子重要扩展的直觉模糊逻辑算子,首先引入了直觉模糊集在特殊格上的等价定义。其次,验证了直觉模糊t-模与s-模的若干重要性质。在此基础上,对两种常用的蕴涵算子：直觉模糊孓蕴涵与直觉模糊R-蕴涵所具有的新性质进行讨论和证明,从而便于直觉模糊逻辑算子的进一步应用。     

基于直觉模糊集和证据理论的群决策方法

针对属性值和权重均为直觉模糊数的多属性决策问题,提出一种基于直觉模糊集和证据理论的群决策方法.首先,对专家给出的每个方案的属性值和属性权重进行证据合成,在此基础上合成每个方案的所有属性值;然后,基于直觉模糊集相似度确定专家的相对权重,修正方案证据,并合成所有专家证据,得到方案的信任区间,根据信任区间的大小对方案进行排序;最后,通过数值案例验证了所提出方法的有效性和合理性.     

区间直觉模糊连续熵及其多属性决策方法

提出了区间直觉模糊连续熵,并且研究了一种新的处理区间直觉模糊多属性决策问题的方法。基于连续有序加权平均（COWA）算子,给出了区间直觉模糊连续熵的概念,并且证明了区间直觉模糊连续熵满足区间直觉模糊熵的公理化定义的四个条件。在此基础上,针对属性权重信息完全未知的决策问题,通过衡量每一属性所含的信息量来确定属性权重。依据备选方案与理想方案间的加权相关系数,给出了一种新的区间直觉模糊多属性决策方法。实验结果验证了新的决策方法的可行性和有效性。     

Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making

The theory of intuitionistic fuzzy sets (IFS) is widely used for dealing with vagueness and the Dempster--Shafer (D-S) evidence theory has a widespread use in multiple criteria decision-making problems under uncertain situation. However, there are many methods to aggregate intuitionistic fuzzy numbers (IFNs), but the aggregation operator to fuse basic probability assignment (BPA) is rare. Power average (P-A) operator, as a powerful operator, is useful and important in information fusion. Motivated by the idea of P-A power, in this paper, a new operator based on the IFS and D-S evidence theory is proposed, which is named as intuitionistic fuzzy evidential power average (IFEPA) aggregation operator. First, an IFN is converted into a BPA, and the uncertainty is measured in D-S evidence theory. Second, the difference between BPAs is measured by Jousselme distance and a satisfying support function is proposed to get the support degree between each other effectively. Then the IFEPA operator is used for aggregating the original IFN and make a more reasonable decision. The proposed method is objective and reasonable because it is completely driven by data once some parameters are required. At the same time, it is novel and interesting. Finally, an application of developed models to the ‘One Belt, One road’ investment decision-making problems is presented to illustrate the effectiveness and feasibility of the proposed operator.     

Some induced intuitionistic fuzzy aggregation operators applied to multi-attribute group decision making

In this paper, we define various induced intuitionistic fuzzy aggregation operators, including induced intuitionistic fuzzy ordered weighted averaging (OWA) operator, induced intuitionistic fuzzy hybrid averaging (I-IFHA) operator, induced interval-valued intuitionistic fuzzy OWA operator, and induced interval-valued intuitionistic fuzzy hybrid averaging (I-IIFHA) operator. We also establish various properties of these operators. And then, an approach based on I-IFHA operator and intuitionistic fuzzy weighted averaging (WA) operator is developed to solve multi-attribute group decision-making (MAGDM) problems. In such problems, attribute weights and the decision makers' (DMs') weights are real numbers and attribute values provided by the DMs are intuitionistic fuzzy numbers (IFNs), and an approach based on I-IIFHA operator and interval-valued intuitionistic fuzzy WA operator is developed to solve MAGDM problems where the attribute values provided by the DMs are interval-valued IFNs. Furthermore, induced intuitionistic fuzzy hybrid geometric operator and induced interval-valued intuitionistic fuzzy hybrid geometric operator are proposed. Finally, a numerical example is presented to illustrate the developed approaches.     

Enhancing PROMETHEE method with intuitionistic fuzzy soft sets

The notion of intuitionistic fuzzy soft sets (IFSSs) provides an effective tool for solving multiple attribute decision making with intuitionistic fuzzy information. The most crucial issue in decision making based on IFSSs is how to derive the ranking of alternatives from the information quantified in terms of intuitionistic fuzzy values. In this study, we propose a new extension of the preference ranking organization method for enrichment evaluation (PROMETHEE), by taking advantage of IFSSs. In addition to presenting a myriad of new notions, such as intuitionistic fuzzy membership (or nonmembership) deviation matrices, intuitionistic fuzzy membership (or nonmembership) preference matrices, and aggregated intuitionistic fuzzy preference matrices, we put more emphasis on the construction of three distinct preference structures and related utility functions on the corresponding weakly ordered sets by considering the positive, negative, and net flows of the alternatives based on the aggregated intuitionistic fuzzy preference matrix. We present a new algorithm for solving multiple attribute decision-making problems with the extended PROMETHEE method based on IFSSs. Moreover, a benchmark problem concerning risk investment is investigated to give a comparative analysis and show the feasibility of our approach.