vague集的多指标决策问题的优劣点法

就vague集的多指标决策问题,引入了方案集的最优点和最劣点的概念,由此给出了vague集多指标决策问题的优劣点法.首先计算各方案的各单指标对理想vague值[1,1]的投影和距离,从而建立评价矩阵,然后根据评价矩阵的各单一指标对应数值的大小得到方案的最优点和最劣点,通过计算各方案到最优点和最劣点的距离以及接近度对方案进行排序,得出最优方案.该方法简单实用,所需信息少,实例验证了该方法的有效性.     

权重信息未知对方案有偏好的vague集多指标决策

就指标权重未知,且对方案有偏好的vague集多指标决策问题,提出了通过使决策者的主观偏好值与属性值的相离度最小来建立最优化模型,从而获得指标的权重.通过将vague值转化为模糊值来建立模糊值矩阵,由模糊值矩阵按各指标对应值的大小对方案进行排序,形成多个线性序,进而由线性序来构造模糊优先矩阵,对其进行截割,得到最优方案.最后通过一个实例说明此方法的具体决策过程.     

基于DEA的vague值的确定性转化及其应用

含糊性和不可分辨性是决策中不确定性的两个方面.Vague集由真、假隶属度两方面来定义,具有强大的表达不精确数据的能力.在介绍Vague集的有关概念的基础上,基于DEA方法,将vague值转化为确定性数据,能够对vague值之间进行比较,且为同时包含vague值和其他定量指标的多目标决策提供思路.     

另一种新的多指标综合评判方法

指出由决策方案在理想方案上的投影及其到理想方案的距离还不能完全确定决策方案的优劣,应该综合考虑决策方案与理想方案的相似系数及其到理想方案的距离.构造了一种基于相似系数和距离的贴近度,给出了一种新的多指标综合评价方法.方法的有效性和合理性通过实例得到了验证.     

基于马氏距离的TOPSIS决策方法及其应用

针对决策指标之间的相关性问题,将马氏距离引入传统TOPSIS方法,提出了基于马氏距离的TOPSIS方法.在此基础上,分析了基于马氏距离改进后贴近度的性质,并以投资决策方案选择为例加以说明.结果表明,基于马氏距离改进的TOPSIS方法对决策数据的非奇异线性变换具有不变性.协方差矩阵体现了决策指标之间的相关性,因而可以有效避免指标的相关性对决策效果的影响.     

基于集值统计原理的多指标灰靶决策方法研究

针对区间数多指标群决策问题,提出一种基于集值统计模型的改进灰靶决策方法。首先利用集值统计模型对多专家的区间评价进行估计,得到符合可信度要求的决策指标样本矩阵。然后利用基于加权广义马氏距离的灰靶决策方法对决策方案进行排序,给出决策样本为区间数群决策矩阵形式的灰靶决策模型。最后通过一个具体的算例给出决策方法的过程,避免了马氏距离不存在的情况,克服了决策指标间的相关性、重要性差异和不同量纲对决策过程和决策结果的影响,方法的可行性与有效性得到验证。     

多指标综合评价方法的改进

由决策方案在理想方案上的投影及其到理想方案的距离还不能完全确定决策方案的优劣,应该综合考虑决策方案与理想方案的相似系数及其到理想方案的距离.     

Vague集的截集及其分解定理

从Fuzzy集的基本理论出发,提出了Fuzzy集的扩展形式—vague集的截集定义和分解定理,进一步表明了Fuzzy集和vague集的相同的本质,丰富了vague集的理论体系,以期拓宽vague集的应用领域.     

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 .     

基于集对分析联系数的区间数型多属性决策投影模型及其在基坑支护方案评价中的应用

多属性决策时因掌握的信息有限,加之问题本身的复杂性、不确定性以及人类思维的模糊性,其属性的精确值往往难以获取,常用区间数形式表示针对多属性决策中各指标区间数不能直接进行大小比较的问题,提出一种基于集对分析联系数的区间数型多属性决策投影模型.模型用备择方案在理想方案上的投影进行集结,根据区间数的误差分布形式,沟通区间数与联系数的关系,根据a+bi型联系数大小比较法则,综合评价方案优劣.以某大厦基坑支护拟用的4种方案优化选择为例,说明方法的应用与模糊优化理论模型和灰色物元分析优化模型进行了对比,得到的结果一致.     

基于Vague集的群决策方法研究

首先介绍了Vague集的基本概念,将直觉模糊集的一些运算规则重新在Vague集上作了定义,提出用Vague值表示的九级语言术语集.接着指出衡量Vague集(值)相似度要考虑的三个因素,提出了新的度量方法.在同时考虑专家决策结果的一致性和专家权重的基础上,提出了汇总各专家Vague意见的方法.最后以一个案例说明了所提出的方法.     

The three dimensions IDC membership distance in vague sets

Based on the dynamic tendency of vague sets and the three-dimension of element membership to a vague set, a method of comparing the two vague sets is put forward by means of set pair analysis. First, according to the positive, negative and uncertain membership of vague sets, the identical discrepancy contrary and weight identical discrepancy contrary membership of vague set are defined; then, their distance is defined between vague sets; last, the identical discrepancy contrary membership distance between vague sets are applied to pattern recognition, which testifies the validity and simplicity of this method.     

Additive functions and their application to uncertain information

For additive functions over ordered sets, a minimum representation is given in case the ordered set has a particular deviation property. Additive functions are known to be special supermodular functions on power sets and in probability calculus, they are related to, for example, capacities and distribution functions of random sets. Their capability to encode vague information is stressed.     

区间数多属性决策的Vague集方法

对属性值为区间数的多属性决策问题,采用Vague集方法进行处理.首先利用区间数决策矩阵的规范化把区间评价值转化为Vague值,然后利用Vague集方法对方案进行排序并选出最优方案.实例分析阐明了本文方法的有效性和实用性.     

多方参与且评价指标集有差异的方案筛选方法

针对多方给出评价指标集存在差异情形的方案筛选问题,提出了一种基于软集理论的方案筛选方法。在该方法中,首先依据各方给出关于备选方案符合评价指标要求的可接受水平,构建各方相应的评价指标子集,进而针对各备选方案符合评价指标要求的结果构建针对各方评价指标子集的软集;然后构建所有方案均符合全部指标要求的软集,进而通过各评价指标子集的软集与所有方案均符合全部指标要求的软集的积运算和算子运算,得到对应评价指标子集的每方可接受的备选方案集;进一步地,通过各方可接受的备选方案集间的并集运算及交集运算,可得到最终的方案筛选结果。最后,通过一个算例说明了本文给出方法的可行性和有效性。     

On the Parameterized OWA Operators for Fuzzy MCDM Based on Vague Set Theory

The family of Ordered Weighted Averaging (OWA) operators, as introduced by Yager, appears to be very useful in multi-criteria decision-making (MCDM). In this paper, we extend a family of parameterized OWA operators to fuzzy MCDM based on vague set theory, where the characteristics of the alternatives are presented by vague sets. These families are specified by a few parameters to aggregate vague values instead of the intersection and union operators proposed by Chen. The proposed method provides a “soft” and expansive way to help the decision maker to make his decisions. Examples are shown to illustrate the procedure of the proposed method at the end of this paper.     

An interactive fuzzy satisficing method for multiobjective 0–1 programming problems with fuzzy numbers through genetic algorithms with double strings

In this paper, by considering the experts' vague or fuzzy understanding of the nature of the parameters in the problem-formulation process, multiobjective 0–1 programming problems involving fuzzy numbers are formulated. Using the a-level sets of fuzzy numbers, the corresponding nonfuzzy α-programming problem is introduced. The fuzzy goals of the decision maker (DM) for the objective functions are quantified by eliciting the corresponding linear membership functions. Through the introduction of an extended Pareto optimality concept, if the DM specifies the degree α and the reference membership values, the corresponding extended Pareto optimal solution can be obtained by solving the augmented minimax problems through genetic algorithms with double strings. Then an interactive fuzzy satisficing method for deriving a satisficing solution for the DM efficiently from an extended Pareto optimal solution set is presented. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.     

Feasible algorithms for approximate reasoning using fuzzy logic

Deductive reasoning with classical logic is hampered when imprecision is present in the variables, although human reasoning can cope quite adequately with vague concepts. A new approach to reasoning which allows imprecise conclusions to be drawn consistently from imprecise premises was introduced by Baldwin [2]. This method is economical in calculation as it avoids the high dimensionality that fuzzy set representations often involve.This paper briefly reviews the method from an operational viewpoint, isolating the individual processes that are used in the method. A feasible algorithm for computing each process is then presented.It is assumed that the reader is familiar with the concept of, and operations on, fuzzy sets introduced by Zadeh [14].