基于超效率DEA模型的中国各地区钢铁行业能源效率及节能减排潜力分析

DEA方法对具有多投入和多产出指标的各个DMU之间的相对效率评价具有独特优势.首先应用超效率DEA模型对2005-2007年间中国各地区钢铁行业的全要素能源效率作出评价,并计算了此间中国各地区钢铁行业的节能减排潜力;然后利用Malmquist效率指数对钢铁行业能源利用效率的变动进行动态分析;最后应用Tobit回归模型对影响钢铁行业节能减排的因素进行了多元分析.     

广义超效率区间DEA模型及其有效性研究

根据样本单元的区间投入、区间产出定义最大样本生产可能集,建立基于最大样本生产可能集的广义超效率区间DEA模型,然后定义了待评价决策单元基于广义超效率区间DEA模型的超效率区间,并讨论了待评价决策单元的有效性,最后通过实例表明了广义超效率区间DEA模型的实用性.     

基于DEA的信用担保运行效率求解方法

针对基于DEA模型的Charnes-Cooper变换需引入阿基米德无穷小量这一主观因素影响效率评价结果,提出基于DEA模型的PSO效率评价方法.通过与Charnes-Cooper变换求解方法的实证比较,发现由于基于DEA的PSO效率评价方法对DEA模型直接求解,最大限度的避免主观因素的影响,使得投入产出指标的选择几乎不会影响信用担保运行效率的评价结果.从而使得效率评价结果更可靠、更符合客观实际.     

基于熵值权重限制的超效率DEA评价模型

为了避免DEA自动分配的权重为零的情况发生,对DEA评价的输入输出指标权重应该加以条件限制.通过利用熵值法分析指标内在的差异性,得到输入输出指标权重的比例关系,将其作为限制条件加入超效率DEA评价模型中,构建基于熵值权重限制的超效率DEA评价模型.此模型既可以客观的揭示指标间的内在关系,又可以使得评价对象实现全排序,评价结果更加符合实际.最后将此模型应用于河北省工程中心的实证分析,结果显示新的模型较传统的DEA模型和超效率DEA模型具有明显的优势.     

投入变量缺失系统DEA效率评价

针对投入变量缺失生产服务系统,提出一种基于DEA的相对效率评价方法.由于该系统的投入无法确知,首先需要依据产出对各决策单元(DMU)进行分组,并将其相对效率分解为组内效率与组间效率.对于组内效率,引人虚拟投入变量利用传统超效率DEA模型进行评价.而对于组间效率,则建立扩展的超效率DEA模型.最终以两类效率之积评价所有决策单元之间的相对效率.理论分析表明:投入缺失系统内决策单元有效的充要条件是其组内效率及其所在组的组间效率均有效.文章最后以基金项目评审为例进行实证分析,说明了本方法的合理性与可行性.     

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.     

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.     

具有偏好锥的面向输出的超效率DEA模型分析

鉴于传统DEA模型无法区分有效决策单元,超效率DEA模型未考虑决策者的偏好,现提出面向输出的权重受限的综合超效率DEA模型及其投影概念,并讨论该模型与其他超效率DEA模型之间的关系.接着,分析模型的最优目标函数值与决策单元有效性之间的关系,并讨论面向输出的权重受限的综合超效投影与多目标规划问题的非支配解之间的关系.最后,通过对中国西部12个地区工业企业科技创新效率综合评价,并与原有方法进行比较研究,得出本文方法更具优势和合理性.     

基于相关性分析与DEA模型的寿险公司效率分析

选取国内较有影响力的八家寿险公司作为研究对象,提出了基于相关性分析的DEA组合评价方法,该方法综合了相关性分析和DEA两种方法的优点.利用相关性分析的方法设计出评价寿险公司经营效率的投入和产出指标.然后综合运用DEA模型对这八家公司的经营效率进行研究,分析影响效率有效性的因素.     

混合DEA模型的区域工业系统运行效率分析与评价

工业系统的运行效率影响着区域经济子系统的增值模式、成长潜力和可持续发展的能力，测度了区域工业系统发展所承载的生态、环境和能源资源成本；本文从投入产出效率评价的视角出发，构建了基于超效率混合DEA模型的评价体系，该模型能够同时处理满足锥性和不满足锥性的评价指标，通过去除决策单元的白限制条件，可以获得决策单元相对效率指数的绝对排序，避免了因评价指标较多而造成的过多决策单元被判定为相对有效，继而致使评价模型失效；评价体系力求突出评价指标的精练性、统计数据的可获性、评价方法的科学性和实践操作性，以黑龙江省1998—2006年度的统计资料为数据分析基础，剖析了评价体系的操作程序和分析方法。     

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.     

The super-efficiency procedure for outlier identification,not for ranking efficient units

In this paper, we conduct simulation experiments to evaluate the performance of two alternative uses of the super-efficiency procedure in Data Envelopment Analysis (DEA). The first is for outlier identification and the second is for ranking efficient units. We find that the ranking procedure does not perform satisfactorily. In fact, the correlations between the true efficiency and the estimated super-efficiency are negative for the subset of efficient observations, and the conventional DEA model performs as well as the super-efficiency DEA model when all observations are considered. However, when data are contaminated with outliers, the use of the super-efficiency model to identify and remove outliers results in more accurate efficiency estimates than those obtained from the conventional DEA estimation model.     

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.     

Equivalent standard DEA models to provide super-efficiency scores

DEA super-efficiency models were introduced originally with the objective of providing a tie-breaking procedure for ranking units rated as efficient in conventional DEA models. This objective has been expanded to include sensitivity analysis, outlier identification and inter-temporal analysis. However, not all units rated as efficient in conventional DEA models have feasible solutions in DEA super-efficiency models. We propose a new super-efficiency model that (a) generates the same super-efficiency scores as conventional super-efficiency models for all units having a feasible solution under the latter, and (b) generates a feasible solution for all units not having a feasible solution under the latter. Empirical examples are provided to compare the two super-efficiency models.     

Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment

A common technique for conducting efficiency analyses consists of a two-stage procedure that combines data envelopment analysis (DEA) with Tobit regression. As the DEA scores are censored at one, this method has the drawback of masking important information at the upper tail of the distribution of scores. In this paper, we present a DEA-based methodology for a two-stage efficiency analysis where the upper bound constraint of one for the efficiency scores is relaxed. This method, super-efficiency DEA, is contrasted with the two-stage approach that employs traditional, bounded DEA scores. We use data from the National Drug Abuse Treatment Survey to examine how the relative efficiency of the treatment units is affected by the organizational structures, operating characteristics and treatment modalities of a nationally representative sample of outpatient substance abuse treatment units. Our results show that the super-efficiency DEA approach offers advantages over the traditional methodology. It is easy to implement, and, for the same sample size provides more information.     

A Euclidean distance-based measure of efficiency in data envelopment analysis

Data envelopment analysis (DEA) has been proven as an excellent data-oriented performance evaluation method when multiple inputs and outputs are present in a set of peer decision-making units (DMUs). Several efficiency measures have been proposed in the DEA literature, see, for instances, radial efficiency measure of Charnes et al. (CCR)(A. Charnes. W.W. Cooper, and E. Rhodes, 1978. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444), Russell graph measure (J.T. Russell, and R. Sirvant. 1999. An enhanced DEA Russell graph efficiency measure. Eur. J. Oper. Res. 115, pp. 596–607) and slack-based measure of Tone (K. Tone, 2001. A slack-based measure of efficiency in DEA. Eur. J. Oper. Res. 130, p. 498–509). In this article, we will propose an Euclidean distance-based measure of efficiency. Then, in order to discriminate the performance of efficient DMUs, an alternative super-efficiency DEA model is proposed. The applicability of the models developed is illustrated in the context of the analysis of gas companies performance.     

Additive super-efficiency in integer-valued data envelopment analysis

Conventional data envelopment analysis (DEA) methods assume that input and output variables are continuous. However, in many real managerial cases, some inputs and/or outputs can only take integer values. Simply rounding the performance targets to the nearest integers can lead to misleading solutions and efficiency evaluation. Addressing this kind of integer-valued data, the current paper proposes models that deal directly with slacks to calculate efficiency and super-efficiency scores when integer values are present. Compared with standard radial models, additive (super-efficiency) models demonstrate higher discrimination power among decision making units, especially for integer-valued data. We use an empirical application in early-stage ventures to illustrate our approach.     

A modified super-efficiency DEA model for infeasibility

The super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. This model provides for a measure of stability of the “efficient” status for frontier DMUs. Under the assumption of variable returns to scale (VRS), the super efficiency model can be infeasible for some efficient DMUs, specifically those at the extremities of the frontier. The current study develops an approach to overcome infeasibility issues. It is shown that when the model is feasible, our approach yields super-efficiency scores that are equivalent to those arising from the original model. For efficient DMUs that are infeasible under the super-efficiency model, our approach yields optimal solutions and scores that characterize the extent of super-efficiency in both inputs and outputs. The newly developed approach is illustrated with two real world data sets.     

Super-efficiency and DEA sensitivity analysis

This paper discusses and reviews the use of super-efficiency approach in data envelopment analysis (DEA) sensitivity analyses. It is shown that super-efficiency score can be decomposed into two data perturbation components of a particular test frontier decision making unit (DMU) and the remaining DMUs. As a result, DEA sensitivity analysis can be done in (1) a general situation where data for a test DMU and data for the remaining DMUs are allowed to vary simultaneously and unequally and (2) the worst-case scenario where the efficiency of the test DMU is deteriorating while the efficiencies of the other DMUs are improving. The sensitivity analysis approach developed in this paper can be applied to DMUs on the entire frontier and to all basic DEA models. Necessary and sufficient conditions for preserving a DMU’s efficiency classification are developed when various data changes are applied to all DMUs. Possible infeasibility of super-efficiency DEA models is only associated with extreme-efficient DMUs and indicates efficiency stability to data perturbations in all DMUs.     

Super-efficiency in DEA by effectiveness of each unit in society

One of the most important topics in management science is determining the efficiency of Decision Making Units (DMUs). The Data Envelopment Analysis (DEA) technique is employed for this purpose. In many DEA models, the best performance of a DMU is indicated by an efficiency score of one. There is often more than one DMU with this efficiency score. To rank and compare efficient units, many methods have been introduced under the name of super-efficiency methods. Among these methods, one can mention Andersen and Petersen’s (1993) [1] super-efficiency model, and the slack-based measure introduced by Tone (2002) [4]. Each of the methods proposed for ranking efficient DMUs has its own advantages and shortcomings. In this paper, we present a super-efficiency method by which units that are more effective and useful in society have better ranks. In fact, in order to determine super-efficiency by this method, the effectiveness of each unit in society is considered rather than the cross-comparison of the units. To do so, we divide the inputs and outputs into two groups, desirable and undesirable, at the discretion of the manager, and assign weights to each input and output. Then we determine the rank of each DMU according to the weights and the desirability of inputs and outputs.