区间数判断矩阵的一致性及权重计算

通过对区间数判断矩阵中的一致性信息的研究,给出了区间数判断矩阵的弱一致性和一致性的一些性质,以及区间权重向量、区间合成权重向量的定义和计算方法,并通过算例说明了算法的实用性及其现实意义.     

区间数互补判断矩阵一致性及其权重算法研究

研究了区间数互补判断矩阵的一致性和权重求解新方法.基于权重可行域定义了区间数互补判断矩阵一致性,给出了判别其是否具有一致性的简洁方法,为区间数互补判断矩阵排序的可靠性提供了更加合理的理论依据;针对区间数互补判断矩阵是否具有一致性,建立了不同的优化模型求解其排序向量,并且给出了具体的算法步骤;最后用算例验证了方法的有效性和适用性.     

基于最大长度区间的区间权重向量标准化方法

区间权重向量在不确定多准则决策分析和AHP中都发挥重要作用,鉴于区间权重向量的不确定性,区间权重向量的标准化问题成为研究的一个热点.在相关研究的基础上,提出基于最大长度区间的区间权重向量标准化方法.首先为了更好理解标准化权重向量,给出标准化区间权重向量的一个等价命题;其次,提出简便的标准化区间权重向量的判定方法,并提出不同情形下,通过调整最大长度区间的区间权重向量的标准化方法;最后,通过算例及与其他方法的对比,说明该方法是简便有效的.     

基于最小偏差和优势度的区间数互补判断矩阵排序法

针对决策信息以区间数互补判断矩阵形式给出的多目标决策问题。首先,给出相对优势度的概念和区间数加性一致性互补判断矩阵的判定定理。其次,建立一个目标规划模型,通过求解该模型得到区间数互补判断矩阵的权重向量,并利用各权重向量的总体相对优势度对方案进行排序,提出了基于最小偏差和优势度的区间数互补判断矩阵排序法。方法的核心是对数值型互补判断矩阵排序方法的拓展。最后,通过实例说明方法的可行性和有效性。     

区间数互反矩阵几何加权集成多属性群决策方法

研究了属性值以区间数表示的群决策问题,提出了区间数决策向量转化为互反判断矩阵的公式,定义了区间数互反判断矩阵几何加权集成算子.在此基础上,提出了区间数多属性群决策的新方法.方法首先针对每一个属性,将各决策者、各方案对应此属性的区间数向量转换为互反判断矩阵,由新定义的集成算子进行集成.由集成区间数矩阵的上界、下界矩阵计算各方案关于此属性的排序向量.由属性权重、可能度和排序公式对方案进行排序.最后给出一个实例进行分析,结果表明了此方法的实用性和可行性.     

一致性区间数判断矩阵的性质及排序

讨论了具有序传递性的区间数判断矩阵的性质,给出了一致性区间数判断矩阵具有序传递性的一个充要条件.在此基础上,证明了一致性区间效判断矩阵的权值可行域中所有向量的分量在不劣于的次序关系下具有完全相同的排列次序.因此,用权值可行域的顶点的算术平均或几何平均作为一致性区间数判断矩阵的排序向量是合理的.     

区间型多属性群体专家权重的确定方法

针对方案偏好和属性值均为区间数的多属性群决策问题，研究了群体专家权重的确定，并提出了一种新的群决策方法．通过定义区间数向量的内积，计算专家评判的相似度和差异度，进而客观地确定了专家的权重．求解最小化主、客观偏差的目标规划模型，得到了属性的权重，利用方案的群体综合属性值给出排序结果．供应商选择的应用实例验证了方法的可行性和合理性．     

区间判断矩阵排序权重的极值点研究

针对区间判断的权重求解问题，利用行和归一化方法，推导排序权重与上三角中各元素之间的关系，得到强序区间判断下排序权重极值的顶点定理以及一般情况下的极值求解方法，为求解区间判断的极值问题提供新的研究思路。文章最后用算例说明用极值点方法求解区间判断权重的过程。     

区间数判断矩阵的排序及一致性改进算法

L.Mikhailov(2003)提出了一个区间数判断矩阵的模糊数学规划排序模型,证明了该模型存在的一个缺陷,即该模型分别只利用上三角判断和下三角判断将会得到不同的权重向量,给出了改进的方法.最后给出了一个简洁有效的一致性修正算法和两个算例.     

一种新的区间数多属性决策权重信息集结方法

针对属性权重以区间数形式给出的多属性决策问题,在充分分析区间数向量的性质后,给出了一种决策模型。模型首先利用区间数权重向量得到决策偏好,然后在离差最大化思想指导下解决此带有决策偏好的决策问题。最后,通过一个实例说明了方法的可行性。     

Stochastic preference analysis in numerical preference relations

Numerical preference relations (NPRs) consisting of numerical judgments can be considered as a general form of the existing preference relations, such as multiplicative preference relations (MPRs), fuzzy preference relations (FPRs), interval MPRs (IV-MPRs) and interval FPRs (IV-FPRs). On the basis of NPRs, we develop a stochastic preference analysis (SPA) method to aid the decision makers (DMs) in decision making. The numerical judgments in NPRs can also be characterized by different probability distributions in accordance with practice. By exploring the judgment space of NPRs, SPA produces several outcomes including the rank acceptability index, the expected priority vector, the expected rank and the confidence factor. The outcomes are obtained by Monte Carlo simulation with at least 95% confidence degree. Based on the outcomes, the DMs can choose some of them which they find most useful to make reliable decisions.     

A new data envelopment analysis method for priority determination and group decision making in the analytic hierarchy process

The DEAHP method for weight deviation and aggregation in the analytic hierarchy process (AHP) has been found flawed and sometimes produces counterintuitive priority vectors for inconsistent pairwise comparison matrices, which makes its application very restrictive. This paper proposes a new data envelopment analysis (DEA) method for priority determination in the AHP and extends it to the group AHP situation. In this new DEA methodology, two specially constructed DEA models that differ from the DEAHP model are used to derive the best local priorities from a pairwise comparison matrix or a group of pairwise comparison matrices no matter whether they are perfectly consistent or inconsistent. The new DEA method produces true weights for perfectly consistent pairwise comparison matrices and the best local priorities that are logical and consistent with decision makers (DMs)’ subjective judgments for inconsistent pairwise comparison matrices. In hierarchical structures, the new DEA method utilizes the simple additive weighting (SAW) method for aggregation of the best local priorities without the need of normalization. Numerical examples are examined throughout the paper to show the advantages of the new DEA methodology and its potential applications in both the AHP and group decision making.     

Analysis of Hospital Capital Decision Alternatives: A Priority Assignment Model

In the not-for-profit sector in general and in the hospital industry in particular, the systematic analysis of capital decision alternatives is often not attempted because those decision making criteria which do not have objective measures, but for which judgments must be rendered, are not explicitly integrated into the analysis of decision alternatives. Rather, judgments on these criteria enter the decision making process in some vague manner after an attempt has been made to develop a quantitative profile for each of the decision alternatives. The purpose of this paper is to examine a priority assignment model which (1) uses judgmental transitivity as a measure of the quality of the judgments rendered, and (2) provides for the mathematical integration of the quality adjusted judgmental information into the development of priority weights. Various operational aspects of the model are examined and illustrated and an example of its application provided.     

A heuristic approach for deriving the priority vector in AHP

This paper proposes an approach for deriving the priority vector from an inconsistent pair-wise comparison matrix through the nearest consistent matrix and experts judgments, which enables balancing the consistency and experts judgments. The developed algorithm for achieving a nearest consistent matrix is based on a logarithmic transformation of the pair-wise comparison matrix, and follows an iterative feedback process that identifies an acceptable level of consistency while complying with experts preferences. Three numerical examples are examined to illustrate applications and advantages of the developed approach.     

An integrated approach for deriving priorities in analytic network process

A multiple objective programming approach for the analytic network process (ANP) is proposed to obtain all local priorities for crisp or interval judgments at one time, even in an inconsistent situation. The weakness of the ANP and fuzzy ANP (FANP) is that the complexity of generating priorities is equal to the number of comparison matrices. In the proposed approach, all sets of crisp priorities for each pairwise comparison matrix can be obtained directly. Moreover, from the outcomes of three examples, the power to reach a limiting supermatrix is less than or equal to the power of the FANP. Thus, the proposed approach can be regarded as an efficient alternative of the fuzzy ANP.     

区间判断的性态测度与调整

区间判断矩阵是区间判断下进行决策的依据，其性态将直接影响排序的正确性。本文在区间判断的凸锥模型基础上，对区间判断矩阵的性态提出了实用测度方法和可采用的调整措施。     

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.     

基于优先关系的多指标区间决策

讨论了指标的权重和效用值都包含区间数的多指标决策问题,构造了方案优先关系的和谐性检验和不和谐性检验,给出了方案间的“强优”和“弱优”关系,研究了方案排序的原理和方法.