三标度AHP法在核应急决策中的应用

核事故发生时,采取正确的应急决策可以减轻事故的后果和危害.考虑到核应急决策的多目标性和模糊性,并针对传统AHP法的不足,利用三标度AHP法建立了多层次应急决策评估模型.模型较大程度的减小了评价的主观性,具有操作性强、稳定性好的特点.将模型与模糊层次模型计算结果进行比较,二者对方案的优劣排序是一致的.但模型在对最优方案的取舍上分辨率较高,对核应急决策具有实际的指导意义.     

对AHP单准则排序方法的改进

层次分析法(AHP)用两两比较的方法,通过构造比例标度判断矩阵为排序提供了一种合理的数据条件;对比例标度判断矩阵进行基于最大特征根的一致性检验,是为了把最大特征值对应的归一化特征向量作为排序向量;但终因理论上不能提供比例标度判断矩阵保序的充分条件,所以特征根排序法受到质疑,AHP需要改进.指出,改进AHP的正确途径是回归基于判断矩阵数据的排序方法研究.排序可行的条件是标度具有可加性;为此定义具有可加性的评分标度,建立标度转换公式将比例标度转换为评分标度;阐述为什么一致性检验无助于正确排序的原因并剖析为什么基于评分标度排序与一致性检验无关;由此建立基于评分标度判断矩阵中数据的评分排序方法.     

正交试验层次分析法

提出了一种正交试验数据分析的新方法——正交试验层次分析法(AnalyticHierarchyProcess,简称AHP).应用层次分析(AHP)对不考虑交互作用的正交试验进行统计分析,得出各因素各水平对试验结果的影响权重,从而确定因素的主次顺序及最佳条件.实验结果表明,应用AHP对正交试验进行分析得到的结论与传统的直观分析法一致.     

判断矩阵与优先权向量之间的冲突现象及其检验

首先分析了判断矩阵与优先权向量之间存在的冲突现象,指出AHP的不一致不仅包括判断矩阵本身的不一致,还包括因判断矩阵与优先权向量之间的冲突而导致的不一致,因此,AHP一致性检验也应该包括冲突误差检验.然后,给出了冲突的定义及其误差的度量指标和计算方法,对目前常见的七种数字标度,运用统计模拟方法,通过随机产生1000个3～9各种阶数的判断矩阵,计算出冲突误差的临界值,从而与传统的一致性检验一起构成更加完备的检验体系.     

浅析模糊AHP中一致性检验的不必要性

模糊AHP用模糊互补矩阵替代AHP的比例标度判断矩阵的意义是,因标度的可加性使基于判断矩阵中数据的排序可行;但是定义模糊一致性矩阵对判断矩阵进行一致性检验是不必要的,调整含逆序的判断矩阵是不合理的.阐述模糊AHP一致性检验的不必要性;论证对含逆序判断矩阵进行调整的不合理性;建立基于评分标度判断矩阵中数据的排序方法,并且排序与判断矩阵具有怎样的一致性程度无关.     

带有熵权-AHP约束锥的DEA模型

在DEA模型中当考虑较多的投入和产出指标时,容易产生决策单元的过度有效性,影响模型的分析效能.因此有相关研究将AHP约束锥引入DEA模型中,不仅有效地限制了DEA模型指标权重的选择自由性,也能更好地将决策者的主观偏好反映在模型中.但引入AHP约束锥产生的主观性会影响到DEA效率评估的客观性.引入熵权法概念,借鉴其客观赋权的思想,将其与AHP约束锥相结合,不仅保留了带有.AHP约束锥的DEA模型的优点,也中和了AHP约束锥的主观性,保证了DEA模型的相对客观性.     

稀土安全评价:集成算法改进与实证

稀土作为我国战略性矿产资源,其安全问题一直是政府和学术界关注与研究的重要课题.建立了稀土安全的概念框架与评价体系,针对传统AHP中九标度法的不足,基于熵值法、加速遗传算法及扩充标度值的层次分析法,提出了一种改进的层次分析法(EV-AGA-EAHP),通过集成模糊C-均值聚类算法和模糊综合评价法,研究提出了一种模糊综合集成评价方法(EV-AGA-EAHP-FCM-FIJ),给出了集成算法及步骤,对2001-2013年我国稀土安全水平进行了评价,提出了保障我国稀土安全的建议.     

一种改进的多维偏好线性规划分析方法

本文对传统的多维偏好线性规划分析方法—LINMAP法进行改进,提出一种新的多维偏好线性规划分析方法,该方法运用加性加权法建立线性规划模型,同时引入任意小正数δ,使得评价结果更加符合决策人偏好集。     

基于DHF算法的装甲车辆动力装置综合评价

针对装甲车辆动力装置的综合评价问题,给出由Delphi法、层次分析法(AHP)和模糊评判法集合而成的DHF算法.首先根据AHP法建立评价对象的层次结构模型,然后结合模糊决策理论,将模糊数融入到AHP法中来表示各位专家所作的评价,使在评价中的主观判断更符合人们的思维习惯和表达方式,其次结合对备选方案等级的满意度评价和准则层的模糊权重得到备选方案的综合评价模糊数,最后用Lee和Li提出的模糊均值法对备选方案进行排序,得到装甲车辆动力装置的综合评价,结果表明该方法是有效的.     

基于DEA/AHP两阶段的工程项目评标方法研究

提出了一种基于DEA/AHP两阶段的工程项目评标方法,首先运用模糊综合评判法来确定定性指标的得分并将其作为定性指标值;其次运用DEA方法计算每一对投标方案相对效率的比值并以此为基础构造判断矩阵;最后运用单一层次的AHP方法对各投标方案进行全排序.通过实例分析表明,方法科学实用,能够为一般的工程项目评标活动提供有效的参考.     

层次分析法在湖南省粮食生产发展对策分析中的应用

本文综述了层次分析法的基本原理和过程，建立了湖南省粮食生产发展对策分析的层次分析模型，对影响湖南省粮食生产发展的多种因素进行了综合排序，其结果有一定的实际指导意义．     

基于DEA-AHP的物流系统绩效评价研究

本文在建立物流系统综合评价层次模型的基础上,基于数据包络分析方法无法考虑决策者偏好及层次分析方法主观性影响过大的缺陷,提出了DEA和AHP相结合的方法对物流系统绩效进行了评价,不同于以往文献对两种方法结合的研究从本质上没有体现决策者偏好的问题,本文提出的方法首先使用AHP方法求出各一层指标的权重,再分别对每个一层指标下的因素使用DEA方法求出各系统的相对效率值,最后将各指标权重和相对效率值结合求出各物流系统的整体效率值并进行排序,该方法在有效地结合两种方法优点的同时,很好的弥补了两种方法的不足,最终的实例分析体现了该方法的适用性和可操作性。     

Another potential shortcoming of AHP

In the last twenty years many features of Saaty’s Analytical Hierarchy Process (AHP) have been criticised, especially the additive hierarchical composition of conventional AHP, which leads to the possibility of occurrence of the Rank Reversal phenomenon (adding an irrelevant alternative may cause a reversal in the ranking at the top). In this paper we show another feature of AHP which may be, and in many application contexts will inneed be, an even stronger shortcoming of the method. It consists in the fact that the addition of indifferent criteria (for which all alternatives perform equally) causes a significant alteration of the aggregated priorities of alternatives, with important consequences. In hierarchies with four or more levels, rank reversal may happen. Since in almost all applications of AHP the set of criteria is not fixed ex-ante but is variable and is constructed in accordance with reasons of relevance and simplicity, almost all applications of AHP are potentially flawed.     

On rank reversal in decision analysis

Analytic hierarchy process (AHP) has been criticized for its possible rank reversal phenomenon caused by the addition or deletion of an alternative. This paper shows the fact that the rank reversal phenomenon occurs not only in the AHP but also in many other decision making approaches such as the Borda–Kendall (BK) method for aggregating ordinal preferences, the simple additive weighting (SAW) method, the technique for order preference by similarity to ideal solution (TOPSIS) method, and the cross-efficiency evaluation method in data envelopment analysis (DEA). Numerical examples are provided to illustrate the rank reversal phenomenon in these popular decision making approaches.     

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.     

层次分析法权重计算方法分析及其应用研究

介绍层次分析法的基本概念,同时也分析了层次分析法权重的计算方法及应用,层次分析法的计算方法有四种方法:几何平均法、算术平均法、特征向量法、最小二乘法,以往的文献利用层次分析法解决实际问题时,都是采用其中的某一种方法求权重向量,不同的方法会导致结果有些偏差,将对一具体实例,采用四种计算方法分别得出权重向量再进行排序和综合分析,这样可以避免采用单一方法所产生的偏差,得出的结论将更全面、更有效。     

Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.     

On modeling dynamic priorities in the analytic hierarchy process using compositional data analysis

In a rapidly changing environment, the priorities derived using the analytic hierarchy process (AHP) approach at one point in time might very likely change in the near future. Thus, in order to adapt to such ever-changing environment, it is of primary importance to be able to follow the change over time as to enable the system to respond differently and continuously over time of its operation. This paper proposes the use of a time-based compositional forecasting method, which is based on the idea of exponential smoothing, to deal with the AHP priority dynamics. The proposed method is particularly useful when there is a limited number of historical data, and might be considered to be more effective and time-efficient compared to that of multivariate time series method. It was also shown that the proposed method provides much greater adaptability in modeling the AHP priorities change over time compared to that of recently developed methods in compositional data research field. The shortcoming of Saaty’s dynamic judgment approach and some limitations of the other existing methods will be discussed. Finally, to substantiate the validity of the proposed method and to give some practical insights, an illustrative case study is provided.     

Influence of aggregation and measurement scale on ranking a compromise alternative in AHP

Analytic Hierarchy Process (AHP) is one of the most popular multi-attribute decision aid methods. However, within AHP, there are several competing preference measurement scales and aggregation techniques. In this paper, we compare these possibilities using a decision problem with an inherent trade-off between two criteria. A decision-maker has to choose among three alternatives: two extremes and one compromise. Six different measurement scales described previously in the literature and the new proposed logarithmic scale are considered for applying the additive and the multiplicative aggregation techniques. The results are compared with the standard consumer choice theory. We find that with the geometric and power scales a compromise is never selected when aggregation is additive and rarely when aggregation is multiplicative, while the logarithmic scale used with the multiplicative aggregation most often selects the compromise that is desirable by consumer choice theory.