基于乘型一致性的毕达哥拉斯模糊偏好关系

在乘型一致性区间值模糊偏好关系和乘型一致性直觉模糊偏好关系启发下,研究了乘型一致性毕达哥拉斯模糊偏好关系。首先,将毕达哥拉斯模糊偏好关系转化为两个等价的区间值模糊偏好关系,通过区间值模糊偏好关系的乘型一致性,定义了毕达哥拉斯模糊偏好关系的乘型一致性。其次,研究了乘型一致性毕达哥拉斯模糊偏好关系的若干性质。然后,研究了毕达哥拉斯模糊偏好关系的排序向量,以及求解乘型一致性和非乘型一致型毕达哥拉斯模糊偏好关系排序向量的方法。最后,通过实例说明了求解排序向量的方法是可行有效的。     

一种基于图论和直觉模糊偏好关系的准则权重求解方法

直觉模糊偏好关系是处理复杂的多目标群决策间题非常有效的一种工具,其元素可通过对准则集内的所有准则实施两两比较确定.文章在已有的直觉模糊偏好关系的积性一致性和BWM方法的基础上,给出一个新的一致性定义,基于此定义提出一种新的基于图论并从直觉模糊偏好关系中导出准则权重的直觉模糊BWM方法.方法分为三个步骤:第一步,邀请专家组根据对称标度对准则集内的准则进行两两比较,给出每个专家的直觉模糊偏好关系,然后,用简化的直觉模糊加权几何算子将所有专家给出的直觉模糊偏好关系融合成群体的直觉模糊偏好关系;第二步,基于有向网络图设计一个算法,根据点的出度及入度对所有的准则进行排序,并鉴别出最重要和最不重要的准则,在文章给出的新的一致性定义基础上,建立了几个max-min优化模型,模型求解以实现从直觉模糊偏好关系中导出准则的优先权重;第三步,使用文章定义的一致性比率公式检验直觉模糊偏好关系的一致性程度以及所导出权重的可靠性;最后,将直觉模糊BWM决策方法运用于解决实际决策问题,探讨了一个评估医院门诊预约系统的例子,评估结果可以为医疗服务部门和需要进行门诊预约的患者提供决策参考.     

基于加性一致的直觉模糊偏好关系一致性检验与调整新方法

本文研究了基于直觉模糊偏好关系的决策问题,提出了一种新的基于加性一致的直觉模糊偏好关系决策方法。首先,基于加性一致的直觉模糊偏好关系,提出了一种新的检验指标。在此基础上,给出了直觉模糊偏好关系可接受加性一致性定义。然后,针对不满足可接受加性一致的直觉模糊偏好关系,设计了一种一致性调整新方法,并证明了方法的收敛性。最后,提出了一种基于直觉模糊偏好关系的决策方法,并通过实例分析和比较分析说明了新方法的可行性和合理性。     

区间数互补判断矩阵的一致性及排序方法研究

研究了区间数互补判断矩阵的一致性和排序方法.首先根据区间数模糊互补判断矩阵的一致性定义给出了其一致性等价定义;然后通过定义导出矩阵,给出了完全一致性判别方法和满意一致指标,并根据此指标给出了一种完全一致性的逼近方法和满意一致的调整方法;最后通过对实数互补判断矩阵权重公式的推广给出了区间数互补判断矩阵的一个权重计算公式.并通过算例说明了此方法的有效性.     

不确定风险偏好下的直觉模糊匹配决策方法

针对匹配信息为直觉模糊偏好关系的双边匹配问题,给出一种考虑主体风险偏好态度的决策方法.首先,定义了加性一致性直觉模糊偏好关系;然后,综合考虑主体给定的偏好关系以及风险偏好参数,建立并求解一种一致性偏差最小的非线性规划模型,从而获得最优排序向量;进而将其作为相应匹配对象的匹配度信息.在此基础上,构建最大化双方匹配度之和的多目标匹配模型,使用极大极小法转化为单目标线性规划模型,求解模型得到匹配结果;最后,通过一个算例表明所提方法的可行性与有效性.     

犹豫直觉模糊偏好关系积性一致性及其在群决策中的应用

模糊偏好关系在群决策中得到了广泛研究,针对犹豫直觉模糊集既能反映决策者偏好和非偏好的信息,又能描述其犹豫心理的特点,提出了犹豫直觉模糊偏好关系及其积性一致性的定义。为了修复不一致的犹豫直觉模糊偏好关系,先构建积性一致性指标,然后提出两种修复方法。最后,将犹豫直觉模糊偏好关系应用到群决策中,通过实例和比较说明了两种修复方法的有效性和合理性。     

次序一致性判断矩阵的相关理论及排序

给出了完全次序一致性的定义和次序一致性矩阵的标准形式,并证明了满意一致性与次序一致性的等价性,然后给出了同时适用于互反与互补两种判断矩阵的完全次序一致性检验及改进的交互式算法,最后在次序一致性的基础上给出了模糊互补判断矩阵排序的一种新方法,并给出了一个算例.     

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.     

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.     

一类广义区间值模糊粗糙集的公理化刻画

首先在一般区间值模糊关系上定义了两个论域上的一类广义区间值模糊粗糙集.借助区间值模糊集的截集给出区间值模糊粗糙上、下近似算子的一般表示.讨论了各种特殊的区间值模糊关系与区间值模糊近似算子性质之间的等价刻画.最后利用公理化方法刻画区间值模糊粗糙集.描述区间值模糊上、下近似算子的公理集保证了生成相同近似算子的区间值模糊关系的存在性.     

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

The paper develops a new intuitionistic fuzzy (IF) programming method to solve group decision making (GDM) problems with interval-valued fuzzy preference relations (IVFPRs). An IF programming problem is formulated to derive the priority weights of alternatives in the context of additive consistent IVFPR. In this problem, the additive consistent conditions are viewed as the IF constraints. Considering decision makers’ (DMs’) risk attitudes, three approaches, including the optimistic, pessimistic and neutral approaches, are proposed to solve the constructed IF programming problem. Subsequently, a new consensus index is defined to measure the similarity between DMs according to their individual IVFPRs. Thereby, DMs’ weights are objectively determined using the consensus index. Combining DMs’ weights with the IF program, a corresponding IF programming method is proposed for GDM with IVFPRs. An example of E-Commerce platform selection is analyzed to illustrate the feasibility and effectiveness of the proposed method. Finally, the IF programming method is further extended to the multiplicative consistent IVFPR.     

Derivation of intuitionistic fuzzy weights based on intuitionistic fuzzy preference relations

This paper proposes linear goal programming models for deriving intuitionistic fuzzy weights from intuitionistic fuzzy preference relations. Novel definitions are put forward to define additive consistency and weak transitivity for intuitionistic fuzzy preference relations, followed by a study of their corresponding properties. For any given normalized intuitionistic fuzzy weight vector, a transformation formula is furnished to convert the weights into a consistent intuitionistic fuzzy preference relation. For any intuitionistic fuzzy preference relation, a linear goal programming model is developed to obtain its intuitionistic fuzzy weights by minimizing its deviation from the converted consistent intuitionistic fuzzy preference relation. This approach is then extended to group decision-making situations. Three numerical examples are provided to illustrate the validity and applicability of the proposed models.     

Iterative algorithms for improving consistency of intuitionistic preference relations

Consistency of preference relations is an important research topic in decision making with preference information. The existing research about consistency mainly focuses on multiplicative preference relations, fuzzy preference relations and linguistic preference relations. Intuitionistic preference relations, each of their elements is composed of a membership degree, a non-membership degree and a hesitation degree, can better reflect the very imprecision of preferences of decision makers. There has been little research on consistency of intuitionistic preference relations up to now, and thus, it is necessary to pay attention to this issue. In this paper, we first propose an approach to constructing the consistent (or approximate consistent) intuitionistic preference relation from any intuitionistic preference relation. Then we develop a convergent iterative algorithm to improve the consistency of an intuitionistic preference relation. Moreover, we investigate the consistency of intuitionistic preference relations in group decision making situations, and show that if all individual intuitionistic preference relations are consistent, then the collective intuitionistic preference relation is also consistent. Moreover, we develop a convergent iterative algorithm to improve the consistency of all individual intuitionistic preference relations. The practicability and effectiveness of the developed algorithms is verified through two examples.     

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.     

Group decision making based on intuitionistic multiplicative aggregation operators

Preference relations are the most common techniques to express decision maker’s preference information over alternatives or criteria. To consistent with the law of diminishing marginal utility, we use the asymmetrical scale instead of the symmetrical one to express the information in intuitionistic fuzzy preference relations, and introduce a new kind of preference relation called the intuitionistic multiplicative preference relation, which contains two parts of information describing the intensity degrees that an alternative is or not priority to another. Some basic operations are introduced, based on which, an aggregation principle is proposed to aggregate the intuitionistic multiplicative preference information, the desirable properties and special cases are further discussed. Choquet Integral and power average are also applied to the aggregation principle to produce the aggregation operators to reflect the correlations of the intuitionistic multiplicative preference information. Finally, a method is given to deal with the group decision making based on intuitionistic multiplicative preference relations.     

直觉模糊变换半群

首先定义了直觉模糊变换半群的概念,给出了一种特殊的直觉模糊变换半群.其次,引入了直觉模糊变换半群上的直觉容许关系,讨论了两个直觉模糊变换半群间的关系,为直觉模糊有限自动机进一步的理论研究提供了代数方法.     

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。     

A method for estimating criteria weights from intuitionistic preference relations

An intuitionistic preference relation is a powerful means to express decision makers’information of intuitionistic preference over criteria in the process of multi-criteria decision making. In this paper, we first define the concept of its consistence and give the equivalent interval fuzzy preference relation of it. Then we develop a method for estimating criteria weights from it, and then extend the method to accommodate group decision making based on them And finally, we use some numerical examples to illustrate the feasibility and validity of the developed method.     

A method based on similarity measures for interactive group decision-making with intuitionistic fuzzy preference relations

In this paper, we study the group decision-making problem in which the preference information given by experts takes the form of intuitionistic fuzzy preference relations, and the information about experts’ weights is completely unknown. We first utilize the intuitionistic fuzzy weighted averaging operator to aggregate all individual intuitionistic fuzzy preference relations into a collective intuitionistic fuzzy preference relation. Then, based on the degree of similarity between the individual intuitionistic fuzzy preference relations and the collective one, we develop an approach to determine the experts’ weights. Furthermore, based on intuitionistic fuzzy preference relations, a practical interactive procedure for group decision-making is proposed, in which the similarity measures between the collective preference relation and intuitionistic fuzzy ideal solution are used to rank the given alternatives. Finally, an illustrative numerical example is given to verify the developed approach.