区间直觉模糊信息的相容性测度和一致性群决策调整

考虑到区间直觉模糊信息的群决策问题,给出区间直觉模糊判断矩阵相容性及其基于基本相容性调整一致性群决策的过程.首先,提出了基于区间直觉模糊判断矩阵的相容性定义及其一些性质.其次,给出了区间直觉模糊判断矩阵的一个相容性指标及衡量区间直觉模糊判断矩阵的相容性准则.再次,利用临界值给出区间直觉模糊信息的一致性群决策调整的过程.最后,利用实例给出了群决策的整体过程,说明了方法的合理性和有效性.     

不完整信息下的直觉模糊软集群决策方法

为了解决具有不完整信息的直觉模糊软集多属性群决策方法的问题,首先在模糊软集和直觉模糊软集的基础之上推广已有软集中缺失数据的填补方法,用来确定不完整信息的填补值。不同决策者的权重值由直觉模糊软集的距离来确定,参数的权重值由熵度量来确定。在群决策的过程中,借助直觉模糊软矩阵集成运算公式,将不同决策者的决策矩阵集成为综合决策矩阵。然后根据对象得分值的不同实现决策。最后,给出实例分析,验证了该方法在实际中有广泛的应用。     

基于泛性模糊数的VIKOR方法研究

建立了一种泛性模糊数可比较的度量,对决策信息通常为泛性模糊数的决策问题进行加工和扩展,提出了基于泛性模糊数不确定信息的VIKOR决策方法,实现了属性为泛性模糊数的多属性群决策及信息融合的目的.     

堰塞湖减灾群决策的直觉模糊距离法

针对堰塞湖减灾群决策信息的不确定性与模糊性,为解决不完全信息条件下属性指标及专家之间存在的相互关联群决策问题,综合运用直觉模糊集理论、海明距离原理和熵权理论,将直觉模糊距离群决策方法运用到堰塞湖减灾决策过程中。工程实例研究表明,运用直觉模糊距离法决策出的最优方案具有较高的区分度和敏感性,增强了决策的客观性、科学性,能为堰塞湖减灾决策提供更为可靠的依据,具有较强的实用价值。     

梯形模糊数直觉模糊Bonferroni平均算子及其应用

本文研究决策信息为梯形模糊数直觉模糊数(TFNIFN)且属性间存在相互关联的多属性群决策(MAGDM)问题,提出一种基于梯形模糊数直觉模糊加权Bonferroni平均(TFNIFWBM)算子的决策方法.首先,介绍了TFNIFN的概念和运算法则,基于这些运算法则和Bonferroni平均(Bonferroni mean,BM)算子,定义了梯形模糊数直觉模糊Bonferroni平均算子和TFNIFWBM算子.然后,研究了这些算子的一些性质,建立基于TFNIFWBM算子的多属性群决策模型,结合排序方法进行决策.最后,将该方法应用在MAGDM中,算例结果表明了该方法的有效性与可行性.     

模糊环境下部分权重信息的多属性群决策方法

针对决策过程中信息是模糊不确定的,属性的权重信息是部分可知的,我们给出了一个新的确定不同专家给出的属性值的权重的方法.该方法能将靠近中心值的属性值分配较大的权重,而给远离中心值的属性值分配较小的权重,从而得到更加合理的综合属性值.为了确定属性的权重信息,我们建立了一个线性规划模型.利用所给的新方法,我们给出了一个新的模糊多属性群决策方法.最后的实例说明方法的有效性和合理性.     

一种基于Shapley值的Pythagorean模糊多属性群决策方法及其应用

针对模糊决策信息环境下的专家权重确定问题提出一种基于Shapley值的Pythagorean模糊多属性群决策方法。本文引入Shapley值和特征函数的定义,提出Pythagorean模糊距离测度和Pythagorean模糊决策误差信息矩阵等概念,并研究它们的性质。进一步,构建基于Shapley值的Pythagorean模糊专家权重确定模型和属性权重确定模型。针对决策信息是以Pythagorean模糊数形式给出的决策问题,提出一种基于Shapley值的Pythagorean模糊多属性群决策方法,并应用到应急救援中,验证了该方法的有效性。     

模糊群决策结果的可信性评估

模糊数是群决策中常用的决策结果表达形式.研究了模糊群决策结果的可信性评估问题.认为群决策结果的可信性与模糊群决策结果的模糊度和一致度存在强相关,因此,模糊群决策结果可信性评估主要就是对群决策结果的模糊度和一致度的计算,创新提出了基于群隶属函数的模糊度和一致度的计算方法,并给出了基于模糊度和一致度的专家模糊数决策结果综合评价值的计算方法,最后,在此基础上求得专家模糊评估结果的可信度和群决策结果的可信度.     

基于Vague集信息的多属性群决策专家水平评判方法

针对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法.首先引进了基于Vague集信息的多属性群决策信息体(即决策信息体)的相关概念,通过决策信息体构造了基于Vague集信息的一致性决策矩阵及模糊熵,其次利用Vague集信息的相似度量以及Vague集信息的模糊熵两种信息不确定性度量方法,对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法,即统计分析方法和模糊熵分析方法,对专家的评判水平进行排序.最后,通过一个算例说明两种方法的一致性、有效性和实用性.     

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.     

区间直觉模糊判断矩阵群决策中的逆判问题

研究了区间直觉模糊判断矩阵的群决策问题.定义了两种区间直觉模糊集相似度公式,给出两种与决策群体意见一致性程度最高的理想区间直觉模糊判断矩阵构造优化方法.利用矩阵对不同专家判断矩阵中相同位置元素的一致性进行分析,并对不同专家的判断信息进行整体相似程度分析,最后通过算例说明了该方法的有效性和实用性.     

基于改进得分函数的直觉梯形模糊数群体多属性决策方法

为考虑群体多属性决策问题中决策人的风险偏好,在直觉梯形模糊数的基础上,利用连续区间有序加权平均算子对直觉梯形模糊数进行化简,使其转换成直觉模糊数。并基于此提出了一种全新的得分函数。从而得到了一种全新的群体多属性决策方法,将其应用于具体算例中,给出了该方法的具体步骤并证明了有效性。     

三角模糊偏好下冲突型多属性群决策方法研究

针对三角模糊偏好下冲突型群决策问题,本文提出一种新的决策方法。在冲突消解阶段,用三角模糊数表示决策专家偏好,定义两三角模糊数型偏好矢量间的相似度,通过计算专家对各个方案的偏好矢量与各方案的群偏好矢量间的相似度,以此为基础定义专家的冲突测度。给出阈值和协商机制调控专家的冲突测度,直到所有的专家的冲突测度都小于给定阈值,进入决策阶段。在决策阶段,利用三角模糊数的期望函数确定属性权重,计算各个方案群偏好矢量与理想方案偏好矢量之间的加权相似度,由加权相似度大小排列决策,选出最优方案。最后给出案例应用,利用Matlab画出各方案的冲突测度图,数值结果表明本文方法的可行性及有效性。     

Logarithmic least squares method to priority for group decision making with incomplete fuzzy preference relations

The aim of this paper is to present a logarithmic least squares method (LLSM) to priority for group decision making with incomplete fuzzy preference relations. We give a reasonable definition of multiplicative consistent for incomplete fuzzy preference relation. We develop the acceptable fuzzy consistency ratio (FCR for short), which is simple and similar to Saaty’s consistency ratio CR for multiplicative fuzzy preference relations. We also extend the LLSM method to the case of individual preference relation with complete information. Finally, some examples are illustrated to show that our method is simple, efficient, and can be performed on computer easily.     

An integrated model-based interactive approach to FMAGDM with incomplete preference information

Incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations are very useful to express decision makers’ incomplete preferences over attributes or alternatives in the process of decision making under fuzzy environments. The aim of this paper is to investigate fuzzy multiple attribute group decision making problems where the attribute values are represented in intuitionistic fuzzy numbers and the information on attribute weights is provided by decision makers by means of one or some of the different preference structures, including weak ranking, strict ranking, difference ranking, multiple ranking, interval numbers, incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations. We transform all individual intuitionistic fuzzy decision matrices into the interval decision matrices and construct their expected decision matrices, and then aggregate all these expected decision matrices into a collective one. We establish an integrated model by unifying the collective decision matrix and all the given different structures of incomplete weight preference information, and develop an integrated model-based approach to interacting with the decision makers so as to adjust all the inconsistent incomplete fuzzy preference relations, inconsistent incomplete linguistic preference relations and inconsistent incomplete multiplicative preference relations into the ones with acceptable consistency. The developed approach can derive the attribute weights and the ranking of the alternatives directly from the integrated model, and thus it has the following prominent characteristics: (1) it does not need to construct the complete fuzzy preference relations, complete linguistic preference relations and complete multiplicative preference relations from the incomplete fuzzy preference relations, incomplete linguistic preference relations and incomplete multiplicative preference relations, respectively; (2) it does not need to unify the different structures of incomplete preferences, and thus can simplify the calculation and avoid distorting the given preference information; and (3) it can sufficiently reflect and adjust the subjective desirability of decision makers in the process of interaction. A practical example is also provided to illustrate the developed approach.     

A Procedure for Decision Making Based on Incomplete Fuzzy Preference Relation

In this paper, we investigate the decision making problem based on fuzzy preference relation with incomplete information. We first introduce incomplete fuzzy preference relation and present some of its desirable properties. We then develop a system of equations. Based on this system of equations, we propose a procedure for decision making based on incomplete fuzzy preference relation, and finally, a numerical example is presented to illustrate the proposed procedure.     

Consistency analysis of triangular fuzzy reciprocal preference relations

In order to simulate the uncertainty associated with impression or vagueness, a decision maker may give her/his judgments by means of triangular fuzzy reciprocal preference relations in the process of decision making. The study of their consistency becomes a very important aspect to avoid a misleading solution. Based on the reciprocity property, this paper proposes a new definition of consistent triangular fuzzy reciprocal preference relations. The new definition is different from that reduced by consistent fuzzy reciprocal preference relations proposed by Buckley (1985). The properties of consistent triangular fuzzy reciprocal preference relations in the light of the new definition are studied in detail. In addition, the shortcomings of the proof procedure of the proposition given by Wang and Chen (2008) are pointed out. And the proposition is reproved by using the new definition of consistent triangular fuzzy reciprocal preference relations. Finally, using the (n − 1) restricted comparison ratios, a method for obtaining consistent triangular fuzzy reciprocal preference relations is proposed, and an algorithm is shown to make a consistent decision ranking. Numerical results are further calculated to illustrate the new definition and the obtained algorithm.     

Fuzzy preference orderings in group decision making

In this paper, some use of fuzzy preference orderings in group decision making is discussed. First, fuzzy preference orderings are defined as fuzzy binary relations satisfying reciprocity and max-min transitivity. Then, particularly in the case where individual preferences are represented by utility functions (utility values), group fuzzy preference orderings of which fuzziness is caused by differences or diversity of individual opinions are defined. Those orderings might be useful for proceeding the group decision making process smoothly, in the same manner as the extended contributive rule method.     

Some models for deriving the priority weights from interval fuzzy preference relations

Interval fuzzy preference relation is a useful tool to express decision maker’s uncertain preference information. How to derive the priority weights from an interval fuzzy preference relation is an interesting and important issue in decision making with interval fuzzy preference relation(s). In this paper, some new concepts such as additive consistent interval fuzzy preference relation, multiplicative consistent interval fuzzy preference relation, etc., are defined. Some simple and practical linear programming models for deriving the priority weights from various interval fuzzy preference relations are established, and two numerical examples are provided to illustrate the developed models.     

考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。