DEA效率分解与规模收益评价

使用输出DEA模型CCR、BCC、FG、ST和WY给出了整体效率的四种分解公式,并利用分解公式判别决策单元规模收益状况(包括拥挤),是对前人研究的整体效率分解公式的一种推广和应用.使用输出DEA模型CCR相对于WY的效率分解公式,可以判断决策单元是否为规模收益不变;使用BCC相对于WY的效率分解公式,可以判别是否出现拥挤.联合使用输出DEA模型FG相对于WY的效率分解公式,和ST相对于WY的效率分解公式,可以判断决策单元是否为规模收益递增、不变、递减或拥挤.     

基于串联两阶段网络DEA模型的供应链规模收益分析

数据包络分析(DEA)是评价供应链系统(Supply chain system)间相对有效性的一种重要的工具,但是传统的DEA不考虑供应链的内部结构,对系统效率评价偏高;而本文所研究两阶段串联供应链系统,考虑把部分中间产品作为最终产品输出,增加额外中间投入的情形.基于所提出的供应链系统结构,本文建立相应的串联结构下的网络DEA模型,并针对所建立模型进行相关理论的研究,给出了串联结构下的生产可能集和规模收益情况判定方法.最后,进行数值实验,以验证我们提出的结论.     

基于SV-AJD模型参数的Malmquist DEA投资组合动态效率评价方法

主要构建了基于SV-AJD模型参数的Malmquist DEA投资组合动态效率评价方法.模型纳入了传统DEA模型未考虑的单位净值相关指标和非单位净值相关指标,并使用本征向量法对5个输入指标和5个输出指标进行加权,综合为单个输入和单个输出指标,克服模型的多维失真.实证过程中,分别对13个类别共172只基金在牛市和熊市情景下的长期稳定收益能力、长期风险控制能力、稀有事件收益能力、稀有事件风险控制能力和DEA效率指标进行比对,分析投资组合特征.最后对市场由牛转熊的过程中,进行Malmquist动态效率指标测算.     

规模收益状况的动态因素分析

基于DEA理论的"交形式"生产可能集,从投入增大和缩小两种角度,对多投入多产出生产系统的各种规模收益状态进行分析,研究部分投入要素变化对部分产出要素的作用效果,即对规模收益状况给予"动态"因素分析,得到判定部分投入与部分产出之间规模收益各种状态的充要条件,从而为研究规模收益各状态的产生原因提供理论依据.     

DEA模型的“动态”规模收益分析与数据挖掘

仅重复使用一个"交形式"的生产可能集,利用"动态"规模收益评价方法来对海量的经济数据进行研究、挖掘,不但可以了解这些海量数据的当前规模收益状况,还可以给出其投入增大或缩小时的规模收益状况.研究是对DEA模型应用领域的拓展,也是对数据挖掘领域的补充.     

只有输出(入)的DEA改进模型及其应用

研究了只有输出(入)的DEA分析方法,针对只有输出(入)DEA模型的不足,重新定义了只有输出(入)的DEA评价方法的有效性,并改进了模型。相对已有的只有输出(入)的DEA模型,该模型充分利用了决策单元的诸输出(入),提高了DEA评价的效果。作为应用,运用新模型对武警防暴队形优选问题进行了有效性分析。     

基于数据包络分析的农业循环经济评价——以黑龙江省各地区为例

基于农业循环经济相关理论,从农业资源投入和经济效益产出的技术经济出发构建农业循环经济评价指标体系,采用DEA方法建模和"投影"方法优化,对黑龙江省13个地区农业循环经济的DEA.有效性和规模收益情况进行了实证分析.结果显示:黑龙江省农业循环经济的DEA.有效有11个地区;非DEA有效有2个地区并经"投影"优化出DEA有效的决策方案.     

数据包络分析中规模效率指数方法的一点注记

原有的规模效率指数方法是基于投入导向的模型进行分析的,有悖于规模收益的定义,应该基于产出导向的模型.为此,把基于投入导向的方法转化为基于产出导向的方法.首先,通过理论证明原方法所提出的判定原则也适用于基于产出导向的方法,但由于使用的模型不同,两种方法存在本质的区别.第二,证明了投入导向和产出导向的BCC模型是不等价的,这直接说明两个方法是不等价的.第三,通过实例表明两种方法将产生不同的计算结果,并通过理论分析了其根本原因.上述结论表明,两种方法在投影方式和计算结果等两方面存在差别.因而,开展规模收益分析应该采用基于产出导向的方法.     

双准则广义DEA模型及其模糊解法

针对单准则广义DEA模型在评价决策单元有效性时的局限性,从输入和输出两个角度同时考虑,建立了双准则广义DEA模型,并应用模糊数学理论给出其求解方法.     

DEA方法在卫生经济学中的应用

自八十年代中期以来 ,对医院和医疗卫生系统进行效率评估引起了人们的广泛关注 ,越来越多的学者开始从事这一领域的研究 .数据包络分析方法 ( DEA)因其可对多输入 ,多输出的系统进行综合效率评价 ,这一特性正符合医院和医疗卫生系统中具有多投入和多产出的特点 ,从而在医院和医疗卫生系统的效率评估中受到重视并有着重要应用 .本文综述了现有文献中有关 DEA方法在医院和医疗卫生系统效率评估的应用 ,对已得到应用的 DEA模型作了简单的介绍 ,同时对 DEA模型和方法的进一步应用给出了建议 .     

A new non-oriented model for classifying flexible measures in DEA

In conventional data envelopment analysis (DEA), measures are classified as either input or output. However, in some real cases there are variables which act as both input and output and are known as flexible measures. Most of the previous suggested models for determining the status of flexible measures are oriented. One important issue of these models is that unlike standard DEA, even under constant returns to scale the input- and output-oriented model may produce different efficiency scores. Also, can be expected a flexible measure is selected as an input variable in one model but an output variable in the other model. In addition, in all of the previous studies did not point to variable returns to scale (VRS), but the VRS assumption is prevailed on many real applications. To deal with these issues, this study proposes a new non-oriented model that not only selects the status of each flexible measure as an input or output but also determines returns to scale status. Then, the aggregate model and an extension with the negative data related to the proposed approach are presented.     

Measurement of returns to scale using non-radial DEA models

There are some specific features of the non-radial data envelopment analysis (DEA) models which cause some problems for the returns to scale measurement. In the scientific literature on DEA, some methods were suggested to deal with the returns to scale measurement in the non-radial DEA models. These methods are based on using Strong Complementary Slackness Conditions from optimization theory. However, our investigation and computational experiments show that such methods increase computational complexity significantly and may generate as optimal, solutions contradicting optimization theory. In this paper, we propose and substantiate a direct method for the returns to scale measurement in the non-radial DEA models. Our computational experiments documented that the proposed method works reliably and efficiently on the real-life data sets.     

Benchmarking the operating efficiency of Asia container ports

The aim of this paper is to explore the operating efficiency, the scale efficiency targets, and the variability of DEA efficiency estimates of Asian container ports. This study applies data envelopment analysis (DEA) with the traditional DEA model, most productive scale size concept, returns to scale approach, and bootstrap method to assess the operating performance, set scale efficient targets, and determine efficiency rankings of Asian container ports. The results of this study can provide port managers with insights into resource allocation, competitive advantages, as well as optimization of the operating performance. The potential applications and strengths of DEA in assessing the Asian container ports are highlighted.     

Super-efficiency infeasibility and zero data in DEA

Lee et al. (2011) and Chen and Liang (2011) develop a data envelopment analysis (DEA) model to address the infeasibility issue in super-efficiency models. In this paper, we point out that their model is feasible when input data are positive but can be infeasible when some of input is zero. Their model is modified so that the new super-efficiency DEA model is always feasible when data are non-negative. Note that zero data can make the super-efficiency model under constant returns to scale (CRS) infeasible. Our discussion is based upon variable returns to scale (VRS) and can be applied to CRS super-efficiency models.     

加性DEA模型与规模收益

[1]给出了用C^2R模型或C^2GS^2模型来判断决策单元的规模收益情况的定理,指出它有时失效。对DEA有效（C^2GS^2）的决策单元,本用加性DEA模型来有效地判断其规模收益情况。     

DEA cross-efficiency evaluation under variable returns to scale

Cross-efficiency evaluation in data envelopment analysis (DEA) has been developed under the assumption of constant returns to scale (CRS), and no valid attempts have been made to apply the cross-efficiency concept to the variable returns to scale (VRS) condition. This is due to the fact that negative VRS cross-efficiency arises for some decision-making units (DMUs). Since there exist many instances that require the use of the VRS DEA model, it is imperative to develop cross-efficiency measures under VRS. We show that negative VRS cross-efficiency is related to free production of outputs. We offer a geometric interpretation of the relationship between the CRS and VRS DEA models. We show that each DMU, via solving the VRS model, seeks an optimal bundle of weights with which its CRS-efficiency score, measured under a translated Cartesian coordinate system, is maximized. We propose that VRS cross-efficiency evaluation should be done via a series of CRS models under translated Cartesian coordinate systems. The current study offers a valid cross-efficiency approach under the assumption of VRS—one of the most common assumptions in performance evaluation done by DEA.     

Measuring super-efficiency in DEA in the presence of infeasibility

Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. Because of the possible infeasibility of super-efficiency DEA model, the use of super-efficiency DEA model has been restricted to the situations where constant returns to scale (CRS) are assumed. It is shown that one of the input-oriented and output-oriented super-efficiency DEA models must be feasible for a any efficient DMU under evaluation if the variable returns to scale (VRS) frontier consists of increasing, constant, and decreasing returns to scale DMUs. We use both input- and output-oriented super-efficiency models to fully characterize the super-efficiency. When super-efficiency is used as an efficiency stability measure, infeasibility means the highest super-efficiency (stability). If super-efficiency is interpreted as input saving or output surplus achieved by a specific efficient DMU, infeasibility does not necessary mean the highest super-efficiency.     

Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit

The purpose of this note is to define a new and more general method to obtain qualitative information about returns to scale for individual observations. The traditional methods developed for estimating returns to scale on non-parametric deterministic reference technologies (Data Envelopment Analysis (DEA) models) are reviewed. A new and more general method that is suitable for all reference technologies is provided. Its usefulness is illustrated by considering variations on an existing non-convex production model, known as the Free Disposal Hull (FDH). When different returns to scale assumptions are introduced into the FDH, then previous methods for determining returns to scale do no longer apply.     

Farrell revisited–Visualizing properties of DEA production frontiers

The contributions of the paper are threefold: (i) compare with mathematical rigour the data envelopment analysis (DEA) model of Charnes, Cooper, and Rhodes and the Farrell model exhibiting constant returns to scale, (ii) reinterpret the contribution of Farrell and Fieldhouse that extended the analysis to variables returns to scale and establish the connection with the approach in Banker, Charnes, and Cooper, and (iii) provide graphical visualization of properties of the frontier function. Both papers by Farrell emphasized the importance of graphical visualization of non-parametric frontier functions, but, to our knowledge, this is seldom followed up in the literature. We use a graphical package (EffiVision) with a numerical representation of the frontier functions, representing the contemporary development of visualization. By making suitable cuts through the DEA frontier in multidimensional space, various graphical representations of features of economic interest can be done. Development of ray average cost function and scale elasticity plots are novel illustrations.     

Calculating scale elasticity in DEA models

In the data envelopment analysis (DEA) efficiency literature, qualitative characterizations of returns to scale (increasing, constant, or decreasing) are most common. In economics it is standard to use the scale elasticity as a quantification of scale properties for a production function representing efficient operations. Our contributions are to review DEA practices, apply the concept of scale elasticity from economic multi-output production theory to DEA piecewise linear frontier production functions, and develop formulas for scale elasticity for radial projections of inefficient observations in the relative interior of fully dimensional facets. The formulas are applied to both constructed and real data and show the differences between scale elasticities for the two valid projections (input and output orientations). Instead of getting qualitative measures of returns to scale only as was done earlier in the DEA literature, we now get a quantitative range of scale elasticity values providing more information to policy-makers.