基于LR模糊数的广义模糊DEA模型及其求解

在广义DEA模型基础上,建立基于LR模糊数的广义模糊DEA模型.通过引入LR模糊数的加权平均值,计算了待评价决策单元能体现决策者偏好的广义模糊效率和平均广义模糊效率,对待评价决策单元进行有效性排序.最后通过实例分析,表明了该模型的实用性.     

不确定环境下基于交叉评价的模糊综合评价方法

由于环境的不确定性,多属性决策中客观数据和主观因素并存,决策者很难做出精准的评判.借鉴DEA交叉评价的思想,将量化数据用交叉评价方法进行处理,得出平均交叉效率值作为模糊综合评价的指标进行二次评价.建立了不确定环境下基于交叉评价的模糊综合评价模型,并通过评价实例验证了模型的客观、全面性.     

基于熵权法的DEA/AR交叉效率知识化制造模式评价

用模糊DEA/AR交叉效率方法,解决制造模式的评价与排序问题.首先,构建了四种不同的DEA/AR交叉效率模型—任意型、对抗型、友好型和博弈型.然后,应用熵权法确定每一种交叉效率模型的最终交叉效率值,目的是用保证域来避免传统模型的权重偏差,从而实现了决策单元的相对有效评价和精确排序.同时,引入奇异指数的概念来衡量最终交叉效率的合理性及可靠性.最后,通过实例验证本文模型和方法的可行性及有效性:与采用简单DEA效率模型进行评价比较,我们的方法会使得效率更加精准、排序更加合理.     

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.     

具有现实约束的均值-方差模糊投资组合绩效评价

本文提出了具有实际约束的均值-方差模糊投资组合优化模型。由于实际投资约束情况,如交易成本、交易量限制、借款限制和基数约束的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合理论的应用带来了很大的困难。基于数据的实际约束的均值-方差模糊投资组合DEA评价模型,文章通过构造前沿面来逼近一般情形下真实的前沿面。最后,通过上海证券市场的实际数据验证了本文方法的合理性与可行性。     

带有灰色关联-AHP约束锥的DEA模型

通过构建带有灰色关联-AHP约束锥,对DEA模型中指标的权重进行约束.可以在保持带有灰色关联约束锥的DEA模型不同指标重要性不同客观性的基础上,又能体现决策者对各指标的偏好程度.通过算例对比分析了该模型与传统DEA、带有灰色关联约束锥的DEA以及带有AHP约束锥的DEA模型的不同特点.     

基于CAR-DEA方法的环境效率评价研究

现有环境效率评价的DEA方法没有考虑多维偏好约束问题,即不同决策单元对不同期望产出和不期望产出的偏好不同. 以地区为例,不同地区对GDP、废水和废气赋予的权重偏好各不相同. 在这种情况下,由于各决策单元的偏好约束不同,形成多维偏好约束集,在传统DEA模型中容易出现无可行解现象. 针对这一问题,基于CAR-DEA方法,结合保证域理论,提出一种解决多维偏好约束集问题的环境效率评价模型. 采用中国工业系统的环境效率评价实例对提出的方法进行了分析和说明.     

基于数据三分法的DEA模型

传统DEA模型在数据两分(偏好与规避)的基础上考察决策单元的有效性,在某些场合不再适用,文章根据决策者的偏好将数据分为三类:规避、偏好、中性类,在此基础上构造评价相对有效性的DEA模型.相较而言,基于数据三分法的DEA模型更具一般性,利用该模型,文章考察了中国近年投入产出绩效问题.     

具有非期望产出的随机DEA模型研究

为解决含有不确定信息的非期望产出效率评价问题,建立了一个非期望产出的随机DEA模型.该模型将非期望产出作为负期望产出进行处理,引入了期望效率值、显著性水平来刻画随机问题,并通过机会约束规划的相关知识将模型转化为确定形式.对模型的最优值的相关性质进行了探讨,说明最优值与期望效率值、显著性水平之间的关系.最后给出数值实验说明该模型的有效性.     

引入特征AHP的PDEA评价模型研究

提出了基于特征AHP的带偏好DEA评价模型,该模型充分利用了带偏好约束锥的DEA方法能反映决策者对各准则的偏好程度,具备研究多个同类样本的"相对优劣性"评价的优势,并可有效地避免了由于引入偏好带来的主观性.最后,通过模型仿真、分析,验证了提出的评价模型的优点.     

3PLS物流设施网络评价的模糊数据包络方法

3PLS物流设施布局直接关系到企业的战略和运作两个层面,对若干初始方案进行评价是保证物流设施布局方案有效性的前提.针对物流设施布局方案评价问题的同质性和模糊性特征,利用三角模糊数表示评价过程中的模糊不确定因素,并提出基于模糊数据包络方法的方案评价框架.通过对算例的评价应用,说明了方法的合理性和有效性.     

Fuzzy data envelopment analysis (DEA): Model and ranking method

Data Envelopment Analysis (DEA) is a very effective method to evaluate the relative efficiency of decision-making units (DMUs). Since the data of production processes cannot be precisely measured in some cases, the uncertain theory has played an important role in DEA. This paper attempts to extend the traditional DEA models to a fuzzy framework, thus producing a fuzzy DEA model based on credibility measure. Following is a method of ranking all the DMUs. In order to solve the fuzzy model, we have designed the hybrid algorithm combined with fuzzy simulation and genetic algorithm. When the inputs and outputs are all trapezoidal or triangular fuzzy variables, the model can be transformed to linear programming. Finally, a numerical example is presented to illustrate the fuzzy DEA model and the method of ranking all the DMUs.     

Cost,Revenue and Profit Efficiency Models in Generalized Fuzzy Data Envelopment Analysis

One of the most important information given by data envelopment analysis models is the cost, revenue and profit efficiency of decision making units (DMUs). Cost efficiency is defined as the ratio of minimum costs to current costs, while revenue efficiency is defined as the ratio of maximum revenue to current revenue of the DMU. This paper presents a framework where data envelopment analysis (DEA) is used to measure cost, revenue and profit efficiency with fuzzy data. In such cases, the classical models cannot be used, because input and output data appear in the form of ranges. When the data are fuzzy, the cost, revenue and profit efficiency measures calculated from the data should be uncertain as well. Fuzzy DEA models emerge as another class of DEA models to account for imprecise inputs and outputs for DMUs. Although several approaches for solving fuzzy DEA models have been developed, numerous deficiencies including the $\alpha$-cut approaches and types of fuzzy numbers must still be improved. This scheme embraces evaluation method based on vector for proposed fuzzy model. This paper proposes generalized cost, revenue and profit efficiency models in fuzzy data envelopment analysis. The practical application of these models is illustrated by a numerical example.     

基于三角形模糊数的DEA模型

基于三角形模糊数,扩展了数据包络分析(Data Envelopment Analysis,DEA)的CCR模型,建立了模糊DEA模型,用于解决决策单元的输入、输出中存在模糊数的问题.     

模糊条件下的决策单元相对有效性评价

研究了模糊条件下决策单元的相对有效性评价问题。首先分析了模糊性因素对决策单元相对有效性的影响;然后根据模糊规划取截集方法和DEA评价的经济含义,给出了模糊DEA模型的求解方法;最后定义了决策单元的模糊DEA有效性以及进行有效性排序的平均置信有效性。文末是一个模糊DEA应用的例子。     

基于DEA交叉评价的模糊有效性度量方法

针对模糊指标与效率指标同时存在的多属性模糊事件评价问题, 提出一种基于DEA交叉效率评价的模糊有效性度量方法.该方法采用DEA交叉效率评价方法对效率指标进行分析, 并模糊化得到的效率值,将其转化为基于评价集的隶属度,与原有模糊指标的评价信息一起构建模糊评价可能集,给出了模糊有效性的概念及相应的评价模型。相对于现有方法而言, 该方法不仅能找出模糊对象的有效性, 及评价结果较低者与有效状态间的差距, 还基于模糊可能集提供了调整信息。     

基于模糊C-OWG算子的模糊DEA模型求解

把Yager和Xu提出的连续区间数据OWG(C-OWG)算子进行拓展,定义了模糊C-OWG(FC-OWG)算子,并研究了它的一些性质.在保持数据一致性的前提下,基于FC-OWG算子,研究了模糊数据包络分析(FDEA)模型向确定型DEA模型的转化问题,并进行了求解.实例表明,新方法计算简单、可行且有效.     

Mean-value at risk portfolio efficiency: approaches based on data envelopment analysis models with negative data and their empirical behaviour

We deal with the problem of an investor who is using a mean-risk model for accessing efficiency of investment opportunities. Our investor employs value at risk on several risk levels at the same time which corresponds to the approach called risk shaping. We review several data envelopment analysis (DEA) models which can deal with negative data. We show that a diversification–consistent extension of the DEA models based on a directional distance measure can be used to identify the Pareto–Koopmans efficient investment opportunities. We derive reformulations as chance constrained, nonlinear and mixed-integer problems under particular assumptions. In the numerical study, we access efficiency of US industry representative portfolios based on empirical distribution of random returns. We employ bootstrap and jackknife to investigate the empirical properties of the efficiency estimators.     

Fuzzy efficiency ranking in fuzzy two-stage data envelopment analysis

Data envelopment analysis (DEA) is a useful tool for efficiency measurement of firms and organizations. Many production systems in the real world are composed of two processes connected in series. Measuring the system efficiency without taking the operation of each process into consideration will obtain misleading results. Two-stage DEA models show the performance of individual processes, thus is more informative than the conventional one-stage models for making decisions. When input and output data are fuzzy numbers, the derived efficiencies become fuzzy as well. This paper proposes a method to rank the fuzzy efficiencies when the exact membership functions of the overall efficiencies derived from fuzzy two-stage model are unknown. By incorporating the fuzzy two-stage model with the fuzzy number ranking method, a pair of nonlinear program is formulated to rank the fuzzy overall efficiency scores of DMUs. Solving the pair of nonlinear programs determines the efficiency rankings. An example of the ranking of the 24 non-life assurance companies in Taiwan is illustrated to explain how the proposed method is applied.