模糊判断矩阵一致性的调整方法

给出了模糊判断矩阵一致性调整的新方法 .该方法是在模糊判断矩阵一致性定义及判定方法的基础上 ,通过构造和分析模糊判断矩阵的调和矩阵 ,进一步给出了将模糊判断矩阵改进为满意一致性矩阵的计算步骤 .最后给出了一个算例 .     

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.     

模糊数互补判断矩阵的乘性一致性研究

指出了文献[13]中乘性一致性区间数互补判断矩阵定义的不足,并重新提出了较为合理的定义,进而定义了乘性一致性模糊数互补判断矩阵.通过引入Q-算子和Q-矩阵,给出了判断一个模糊数互补判断矩阵是否满足乘性一致性的较为实用的检验方法.最后通过一个算例说明了此方法的可行性和简洁性.     

基于广义一致性变换的相异标度法构造的判断矩阵的排序

针对不同标度构造的判断矩阵的一致性检验以及排序问题,给出了判断矩阵广义一致性变换的定义,并论证了判断矩阵经广义一致性变换后所具有的性质通过对比分析指出研究结论具有更广的应用范围,深化了对参数β的理解,给出了参数β取值范围的一个合理区间.进而归纳出由不同标度法构造的判断矩阵的具体的广义一致性变换及其排序方法.     

混合互补判断矩阵一致性研究

给出混合互补判断矩阵一致性的定义和判别加性一致性的方法.定义了核算子、核矩阵,对带有精确数、三角模糊数和梯形模糊数的混合互补判断矩阵给出基于核矩阵的一致性调整方法,调整量可以是精确数也可以是模糊数.最后给出一个应用实例.     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

两类模糊数判断矩阵的转换关系及一致性研究

研究了三角模糊数互反和互补判断矩阵的相互转换和一致性问题.提出了三角模糊数互反判断矩阵完全一致性的定义以及三角模糊数互补判断矩阵加性一致性和乘性一致性的定义,给出了两类模糊数判断矩阵相互转化的公式,论证了转换公式对判断矩阵一致性的保持关系.最后,基于一致性模糊数判断矩阵元素和排序权值的关系,建立了两个方案排序的非线性规划模型.     

关于判断矩阵的一致性问题

本文讨论了层次分析法中判断矩阵的一致性问题.同时,给出一个寻求使判断矩阵按任意精度要求达到任意满意一致性的方法.     

Fuzzy判断矩阵的一致性修正

本文给出了一种由分析者求解和决策者修正判断交替进行寻求一致性Fuzzy判断矩 阵的方法,证明了按照此方法。在对Fuzzy判断矩阵进行有限次修正之后,可以得到一个满足 给定精度要求的一致性Fuzzy判断矩阵.     

基于模糊互补判断矩阵的一致性调整新方法

针对模糊互补判断矩阵的乘性一致性问题,本文从模糊乘性一致矩阵的定义出发, 首先给出了一种衡量判断矩阵一致性程度的新指标,然后综合利用专家给出的直接判断信息和间接判断信息,提出了一种改善模糊互补判断矩阵一致性的新方法,最后通过一个算例说明了此方法的可行性和简洁性.     

A simple method to improve the consistency ratio of the pair-wise comparison matrix in ANP

Tests of consistency for the pair-wise comparison matrices have been studied extensively since AHP was introduced by Saaty in 1970s. However, existing methods are either too complicated to be applied in the revising process of the inconsistent comparison matrix or are difficult to preserve most of the original comparison information due to the use of a new pair-wise comparison matrix. Those methods might work for AHP but not for ANP as the comparison matrix of ANP needs to be strictly consistent. To improve the consistency ratio, this paper proposes a simple method, which combines the theorem of matrix multiplication, vectors dot product, and the definition of consistent pair-wise comparison matrix, to identify the inconsistent elements. The correctness of the proposed method is proved mathematically. The experimental studies have also shown that the proposed method is accurate and efficient in decision maker’s revising process to satisfy the consistency requirements of AHP/ANP.     

数控车床可靠性分配中的单准则排序方法

数控车床可靠性分配模型是一个层次结构,可靠性分配的关键技术是确定结构底层指标关于顶层目标的重要度排序,其前提条件是单准则排序已知.AHP通过构造"两两比较"的"1~9"比例标度判断矩阵A_n为单准则排序提供了合理的数据条件;但是基于A_n一致性检验的特征根排序法因临界值的确定缺乏必要理论基础而受到质疑.改进AHP的FAHP因为没有摆脱"一致性检验"的干扰所以改进并不成功.为了建立与"一致性"无关的单准则排序方法定义具有可加性的评分标度概念,通过标度转换将比例标度判断矩阵A_n转化为评分标度判断矩阵B_n,利用评分标度的可加性在准则C下对n个比较对象排序.因为B_n不是正矩阵所以不存在"一致性概念",因此基于评分标度判断矩阵的排序与"一致性"无关.     

Estimation of missing judgments in AHP pairwise matrices using a neural network-based model

Selecting relevant features to make a decision and expressing the relationships between these features is not a simple task. The decision maker must precisely define the alternatives and criteria which are more important for the decision making process. The Analytic Hierarchy Process (AHP) uses hierarchical structures to facilitate this process. The comparison is realized using pairwise matrices, which are filled in according to the decision maker judgments. Subsequently, matrix consistency is tested and priorities are obtained by calculating the matrix principal eigenvector. Given an incomplete pairwise matrix, two procedures must be performed: first, it must be completed with suitable values for the missing entries and, second, the matrix must be improved until a satisfactory level of consistency is reached. Several methods are used to fill in missing entries for incomplete pairwise matrices with correct comparison values. Additionally, once pairwise matrices are complete and if comparison judgments between pairs are not consistent, some methods must be used to improve the matrix consistency and, therefore, to obtain coherent results. In this paper a model based on the Multi-Layer Perceptron (MLP) neural network is presented. Given an AHP pairwise matrix, this model is capable of completing missing values and improving the matrix consistency at the same time.     

一种基于决策矩阵的DST-AHP多属性决策方法

针对层次分析法决策时存在两两判断、一致性检验次数过多和判断矩阵残缺性等问题,本文提出了一种基于决策矩阵的DST-AHP多属性决策方法。该方法结合决策矩阵的特征值,构建DST-AHP方法层次结构模型和判断矩阵,并根据判断矩阵定义不同属性下各焦元的基本概率分配函数;然后利用Dempster合成法则对基本概率分配函数值进行合成,依据合成后值对方案进行排序。最后对AHP和DST-AHP两种方法进行比较分析,说明该方法的有效性。     

Note on group consistency in analytic hierarchy process

We study the paper of Xu [Z. Xu, On consistency of the weighted geometric mean complex judgement matrix in AHP, European Journal of Operational Research 126 (2000) 683–687] for the group consistency in analytic hierarchy process of multicriteria decision-making. The purpose of this note is threefold. First, we point out the questionable results in this paper. Second, for three by three comparison matrices, we provide a patchwork for his method. Third, we constructed a counter example to show that in general his method is wrong. Numerical examples are provided to illustrate our findings. If there are four or more alternatives, then we may advise researchers to ignore his results to avoid questionable estimation of group consistency.     

Achieving matrix consistency in AHP through linearization

Matrices used in the analytic hierarchy process (AHP) compile expert knowledge as pairwise comparisons among various criteria and alternatives in decision-making problems. Many items are usually considered in the same comparison process and so judgment is not completely consistent – and sometimes the level of consistency may be unacceptable. Different methods have been used in the literature to achieve consistency for an inconsistent matrix. In this paper we use a linearization technique that provides the closest consistent matrix to a given inconsistent matrix using orthogonal projection in a linear space. As a result, consistency can be achieved in a closed form. This is simpler and cheaper than for methods relying on optimisation, which are iterative by nature. We apply the process to a real-world decision-making problem in an important industrial context, namely, management of water supply systems regarding leakage policies – an aspect of water management to which great sums of money are devoted every year worldwide.     

模糊判断矩阵排序向量的确定方法研究

首先给出模糊判断矩阵的两种一致性的定义。然后分析现有确定模糊判断矩阵排序向量的方法的特点及存在的问题，在此基础上，系统研究确定模糊判断矩阵排序向量的两类方法，第一类方法是先将模糊判断矩阵转化为AHP判断矩阵，然后将后者的排序向量作为前者的排序向量；另一类方法是直接由一致性或具有满意一致性的模糊判断矩阵计算排序向量。最后用算例说明所提出方法的应用。     

A quality control approach to consistency paradoxes in AHP

The Analytic Hierarchy Process (AHP) requires a specific consistency check of the pairwise comparisons in order to ensure that the decision maker is being neither inconsistent nor random in his or her pairwise comparisons. However, there are many situations where the decision maker has been reasonable, logical and non-random in making the pairwise comparison and yet will fail the consistency check. This paper argues against the use of the standard consistency check. If a consistency test is to be done, a quality control approach is recommended.     

带概率判断和模糊区间判断的一种排序算法

对于 AHP中一类判断为模糊、不确定性问题 ,用随机变量和模糊区间描述其判断 ,采用 0 .1～ 0 .9标度 ,建立模糊互补判断矩阵 ,利用数学变换得到模糊一致性判断矩阵 ,给出排序向量算法及公式 ,便于实际应用     

A simple formula to find the closest consistent matrix to a reciprocal matrix

Achieving consistency in pair-wise comparisons between decision elements given by experts or stakeholders is of paramount importance in decision-making based on the AHP methodology. Several alternatives to improve consistency have been proposed in the literature. The linearization method (Benítez et al., 2011 [10]), derives a consistent matrix based on an original matrix of comparisons through a suitable orthogonal projection expressed in terms of a Fourier-like expansion. We propose a formula that provides in a very simple manner the consistent matrix closest to a reciprocal (inconsistent) matrix. In addition, this formula is computationally efficient since it only uses sums to perform the calculations. A corollary of the main result shows that the normalized vector of the vector, whose components are the geometric means of the rows of a comparison matrix, gives the priority vector only for consistent matrices.