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。     

基于超效率DEA的学生学习效果评定

针对目前高职院校由于同班级生源层次较多产生的学生总评成绩评定存在的问题,该研究同时兼顾对学生考试成绩和学习效率的考查,使用综合经典DEA(CCR-DEA)与超效率DEA(SE-DEA)方法构建高职院校学生学习效果评价模型.随机抽取某班学生的数学成绩为例,采用构建模型对其进行实证综合评价,测试结果表明超效率DEA方法对学生总评成绩评价效果理想.     

具有包容关系的结构异质DEA效率评价方法

指标结构同质是数据包络分析(DEA)方法的基本假设之一;然而,现实问题的复杂性使得该假设常常难以完全被满足.针对具有包容关系的产出结构异质问题,通过解析决策单元(DMU)之间生产结构的内在关系来构建一种分阶段的DEA效率评价方法.该方法充分考虑了不同结构DMU的主观偏好,较好地规避了传统DEA方法在结构异质DMU效率评价过程中的不公平性.随后,该方法分别被拓展至投入结构异质和多重结构异质的情境中.最后,通过两个算例来说明本文方法的有效性与实用性.     

基于关联网络DEA的供应链协调程度评价

本文给出了一种供应链网络协调程度的评价方法。研究思路是:首先,关联网络DEA能求出供应链网络上各个决策单元的DEA效率。其次,假设供应链网络实现了协调,那么网络上所有企业的DEA效率应该都为1。因此可以用供应链网络的实际DEA效率与协调状态(即所有决策单元DEA效率均为1)的差距,来评价供应链网络的协调程度。基于这一思路,本文研究了一个由3个供应商、2个制造商和3个零售商组成的供应链网络的协调评价问题,构建了基于关联网络DEA的类方差协调评价模型,实现了对供应链效率和协调性的同时测量,并通过数值算例验证了方法的实用性和有效性。     

具有非期望产出的混联网络DEA模型

数据包络分析(DEA)是评价系统相对有效性的分析方法,网络DEA模型在评价企业的经济效益、管理效益等实际问题中有着广泛的应用.在网络DEA模型的基础上考虑非期望产出要素,提出了具有非期望产出的混联网络DEA模型.研究了新模型的系统弱DEA有效与各子阶段弱DEA有效之间的关系,找到了无效决策单元的无效阶段,通过有针对性的改进能够提高系统的整体效率.最后通过数值算例验证了模型的可行性.     

基于输出的C~2GS~2模型对学习成绩(效果)的评价

学生学习成绩的评价是提高教学质量和学生学习能力的一个重要方面,传统的评价方法只能从最后成绩的优劣给出结论,未能考虑学生的学习基础.分别利用DEA(C2R)模型和基于输出DEA(C2GS2)模型对学生学习效果进行评价,模型仿真结果充分表明该方法更客观、公正,避免了只考虑最终成绩不考虑入学基础以及由于各科出题难度和评分不统一时造成的评价偏颇问题.     

基于网络DEA的供应链效率评价

数据包络分析(DEA)是评价决策单元(DMU)相对有效性的一种重要的工具.供应链效率评价是供应链管理的重要内容,对于衡量供应链目标的实现程度及提供经营决策都具有十分重要的意义.DEA是用于评价供应链效率的一种有效的方法,但是传统的DEA不考虑供应链的内部结构,对系统的相对效率评价偏高;而研究具有串联结构的供应链,考虑把部分中间产品作为最终产品输出,并增加额外中间投入的情形.基于此,建立相应的网络DEA模型,进行了相关理论的研究,给出了弱网络DEA有效的充分条件,以及整个系统弱网络DEA有效与各个子系统网络DEA有效的关系等.     

基于DEA改进前沿面的输入拥塞分析

输入拥塞分析为投入产出分析提供了另一种视角，即通过减少冗余输入以增加输出。当前，对生产单位进行输入拥塞分析的主要方法是基于数据包络分析(DEA)模型的BCSW模型。BCSW模型的基本思想是将被评价单位与DEA前沿面上的有效率单位进行比较从而得出输入拥塞的值。但该方法忽略了DEA前沿面的数据敏感性问题，即前沿面上单位发生极小变动会导致评价结果的巨大改变，导致分析结果缺乏稳健性。本文提出了一种启发式方法，从改进DEA前沿面的角度出发，通过在误差范围内找到最佳前沿面，使输入拥塞分析结果更加合理。方法的提出从DEA前沿面的数据敏感性问题的原因出发，利用最小二乘法基本思想确定DEA最佳前沿面所需具备的性质。之后在该性质下，利用超效率DEA模型的思想和方法，解决DEA前沿面存在的问题，确定了该启发式方法。最后，本文在该方法所确定的最佳前沿面的基础上，利用BCSW模型进行输入拥塞分析，在实例数据上取得了相对于原始BCSW模型更合理也更具解释性的结果，证实了利用输入拥塞分析时，DEA前沿面确实存在的问题以及解决该问题对输入拥塞分析方法的改进作用。     

改进交叉评价DEA在模糊综合评价中的应用

借鉴对抗型交叉评价的思想,首先利用对抗型交叉评价DEA(数据包络分析)模型对模糊综合评价的量化指标进行评价,三角模糊化后将其作为模糊综合评价量化指标的输入与非量化指标数据合成进行二次评价,以此建构了一种基于对抗型交叉评价DEA的模糊综合评价方法.方法可从根本上解决已有评价方法中模糊量化结果的不确定性问题,使客观数据与主观因素并存的多属性决策更加可靠.最后,通过算例说明了方法的应用.     

基于复合DEA模型高校办学效益评价方法研究

传统DEA只能在固定投入(或产出)的情况下,将产出(或投入)尽量扩大(或缩小),而造成决策单元非DEA有效的原因,既有投入方面的因素,也有产出方面的因素,是由投入和产出两方面的原因共同造成的,而不仅仅是一方面的原因.本文考虑投入和产出两方面的因素,构造了复合DEA模型,并研究了基于复合DEA模型高校办学效益评价方法.     

Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there are two types of network DEA models. One is developed under the standard multiplier DEA models based upon the DEA ratio efficiency, and the other under the envelopment DEA models based upon production possibility sets. While the multiplier and envelopment DEA models are dual models and equivalent under the standard DEA, such is not necessarily true for the two types of network DEA models. Pitfalls in network DEA are discussed with respect to the determination of divisional efficiency, frontier type, and projections. We point out that the envelopment-based network DEA model should be used for determining the frontier projection for inefficient DMUs while the multiplier-based network DEA model should be used for determining the divisional efficiency. Finally, we demonstrate that under general network structures, the multiplier and envelopment network DEA models are two different approaches. The divisional efficiency obtained from the multiplier network DEA model can be infeasible in the envelopment network DEA model. This indicates that these two types of network DEA models use different concepts of efficiency. We further demonstrate that the envelopment model’s divisional efficiency may actually be the overall efficiency.     

Output–input ratio analysis and DEA frontier

Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficient frontiers for peer decision making units (DMUs). The ability of identifying frontier DMUs prior to the DEA calculation is of extreme importance to an effective and efficient DEA computation. In this paper, we present mathematical properties which characterize the inherent relationships between DEA frontier DMUs and output–input ratios. It is shown that top-ranked performance by ratio analysis is a DEA frontier point. This in turn allows identification of membership of frontier DMUs without solving a DEA program. Such finding is useful in streamlining the solution of DEA.     

A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)

This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.     

链式网络DEA模型

数据包络分析(DEA)是评价决策单元(DMU)相对有效性的一种工具,现已得到广泛的应用.传统的DEA不考虑系统内部结构,而是将系统作为一个"黑箱"来度量效率.针对多阶段网络结构提出一个新的网络DEA模型—链式网络DEA模型.研究网络决策单元的网络DEA有效性及各个阶段的弱DEA有效性之间的关系,给出了网络DEA有效的充分必要条件.若网络决策单元不是网络DEA有效的,根据模型可以指出系统在哪些阶段是无效的.     

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment.     

关于DEA有效性“新方法”的探讨

主要指出文献[1],[2]中所用的"新方法"不能完全区分决策单元的DEA有效性和弱DEA有效性.同时,"新方法"中所使用的DEA模型(即文献[3]中超效率DEA模型)的最优解不一定存在,这也是"新方法"使用中的一大缺陷.本文同时指出"新方法"虽然是可以扩充的,但扩充后,某些"新模型"仍然会出现上述问题.如果单纯的去评价决策单元的DEA有效性、弱DEA有效性和非弱DEA有效性时,建议还是使用传统的经典模型为好;如果需要进一步对DEA有效性再进行分析,是可以象最早提出超效率DEA模型的文献[3]中那样去应用超效率DEA模型。     

A game theoretical model of DEA efficiency

Motivated by the inherent competitive nature of the DEA efficiency assessment process, some effort has been made to relate DEA models to game theory. Game theory is considered not only a more natural source of representing competitive situations, but also beneficial in revealing additional insights into practical efficiency analysis. Past studies are limited to connecting efficiency games to some particular versions of DEA models. The generalised DEA model considered in this study unifies various important DEA models and presents a basic formulation for the DEA family. By introducing a generalised convex cone constrained efficiency game model in assembling the generalised DEA model, a rigorous connection between game theory and the DEA family is established. We prove the existence of optimal strategies in the generalised efficiency game. We show the equivalence between game efficiency and DEA efficiency. We also provide convex programming models for determination of the optimal strategies of the proposed games, and show that the game efficiency unit corresponds to the non-dominated solution in its corresponding multi-objective programming problem. Our study largely extends the latest developments in this area. The significance of such an extension is for research and applications of both game theory and DEA.     

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.     

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.     

用超效率综合DEA模型来研究DEA有效性

对超效率综合DEA模型,有三个定理来判断其不可行性,其中一个定理基于加性模型来判断,并证明:当模型不可行时被评决策单元的扩展DEA有效性,由此给出了对扩展DEA有效的决策单元排序的方法,此外,对不含非阿基米德无穷小的基于输入(输出)的超效率综合DEA模型,当其最优值为1时,有一个定理来判断被评单元的DEA有效性.