基于半自由处置变量的链型系统DEA模型

对网络数据包络分析(DEA)模型的一个特例即链型系统的DEA模型进行研究。传统DEA模型总是假设非有效的决策单元的所有输入(或输出)可以同比例改变以提高性能,但这并不符合所有的情形。在两阶段生产系统DEA模型中引入半自由处置变量,建立新模型。新模型将使非有效的决策单元和子决策单元可以根据管理需要有目的地分比例控制各输入和输出变量,一个简单的例子证明了模型比之传统方法更能发现非有效的决策单元并指明其性能改善的方向。最后,该方法被运用到商业银行效率评价的实践中。     

Integrating dual-role variables in data envelopment analysis

Traditional data envelopment analysis (DEA) theory assumes that decision variables are regarded as inputs or outputs, and no variable can play the roles of both an input and an output at the same time. In fact, there exist some variables that work as inputs and outputs simultaneously and are called dual-role variables. Traditional DEA models cannot be used to appraise the performance of decision making units containing dual-role variables. The paper analyzes the structure and properties of the production systems comprising dual-role variables, and proposes a DEA model integrating dual-role variables. Finally the proposed model is illustrated to evaluate the efficiency of university departments.       

区间DEA的一种改进的充分排序方法

区间DEA中决策制定单元（DMU）的效率值是一个区间数，传统的区间DEA研究只能以区间效率值将DMU分为有效、部分有效和无效三类，而对区间DEA中DMU的排序研究较少。本文以两个区间数大小的可能度为基础，并且考虑了区间数退化为实数的情况。通过排序向量从而对输入输出中含有区间数的决策单元进行排序，通过将超效率DEA模型进行区间扩展，从而对同为有效的DMU进行排序，以迭到对所有的DMU进行充分排序的目的。最后给出了一个算例。     

一种改进的环境效率评价ISBM-DEA模型及其算例

期望产出的生产过程通常伴随着对环境造成一定程度破坏的副产品的生成,但经典的DEA模型很难解决考虑非期望产出的效率评价问题。本文给出了一种改进的环境效率评价ISBM-DEA模型,并构造一个算例,将这一改进的模型与环境评价经典模型SBM的评价结果进行对比,结果表明新模型的计算结论和使用SBM得到的效率评价值相关程度很高,而且比SBM模型更加注重非期望产出对生产效率的影响,说明此模型具有更为广泛的应用价值。     

不同决策环境下的区间属性决策单元效率全排序

建立了不同决策环境下区间属性决策单元效率全排序方法.本文结合超效率思想把不确定性的区间数据包络模型转化成一对确定性的超效率数据包络模型,以获得不存在效率拥挤的起效率区同;然后,根据不同决策环境的需要,分别借助赫威斯决策准则和区间数比较的可能度理论,建立了可供选择的决策单元区间效率水平的全排序方法.最后通过两个典型实例说明本文方法的优越性.     

基于加性模型使决策单元变为DEA有效并求C~2R或C~2GS~2模型的最优值的方法

本文给出了基于数据包络分析中的加性模型求Ｃ２Ｒ或Ｃ２ＧＳ２模型最优值的方法、以及在ＤＥＡ有效（Ｃ２Ｒ或Ｃ２ＧＳ２）的决策单元保持其有效性的条件下对某个决策单元通过改变它的输入或输出使它变为ＤＥＡ有效的方法。     

基于期望值方法的随机DEA综合模型及应用

对环境约束下包含不可控输入、非期望输出的DEA综合模型的相对有效性进行研究．给出决策单元有效的定义及定理。进一步提出了随机条件下决策单元的期望有效性指数概念和相对有效性评价的期望值方法，最后给出了计算实例。     

Fuzzy data envelopment analysis approach based on sample decision making units

The conventional data envelopment analysis (DEA)measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs.In real-world problems,however,inputs and ou...     

“混合的”数据包络分析模型的灵敏度分析

对文献［１］所讨论的“混合的”ＤＥＡ模型,进行灵敏度分析,对原来ＤＥＡ有效的决策单元给出了在改变其输入与输出后仍保持ＤＥＡ有效的充分条件。     

DEA数据标准化方法及其在方向距离函数模型中的应用

方向距离函数是对径向DEA模型的推广,它能够方便地处理存在非期望产出的情况,但其效率测量未解决单位不变性问题,这是制约方向距离函数在实践中应用的一个障碍。DEA数据标准化方法为效率测量方法提供了保持单位不变性的通用方法,同时采用标准化数据后,径向和非径向模型的效率测量结果保持不变。基于DEA数据标准化方法建立了具有单位不变性的方向距离函数效率测量方法。     

基于DEA方法的多指标评价

针对同时含有成本型指标、效益型指标和固定型指标的多指标评价问题,利用DEA方法进行评价,给出了多指标评价的DEA模型。并且对于DEA方法中部分指标过于理想化问题,考虑了权重限制,使其产生更加合理的输入和输出权重。在出现多个有效评价对象的情况下,利用超效率DEA模型对有效评价对象进行充分比较和排序,最后给出了评价实例。     

Efficiency decomposition with shared inputs and outputs in two-stage DEA

Data envelopment analysis (DEA) is an effective non-parametric method for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and outputs. In many real situations, the internal structure of DMUs is a two-stage network process with shared inputs used in both stages and common outputs produced by the both stages. For example, hospitals have a two-stage network structure. Stage 1 consumes resources such as information technology system, plant, equipment and admin personnel to generate outputs such as medical records, laundry and housekeeping. Stage 2 consumes the same set of resources used by stage 1 (named shared inputs) and the outputs generated by stage 1 (named intermediate measures) to provide patient services. Besides, some of outputs, for instance, patient satisfaction degrees, are generated by the two individual stages together (named shared outputs). Since some of shared inputs and outputs are hard split up and allocated to each individual stage, it needs to develop two-stage DEA methods for evaluating the performance of two-stage network processes in such problems. This paper extends the centralized model to measure the DEA efficiency of the two-stage process with non splittable shared inputs and outputs. A weighted additive approach is used to combine the two individual stages. Moreover, additive efficiency decomposition models are developed to simultaneously evaluate the maximal and the minimal achievable efficiencies for the individual stages. Finally, an example of 17 city branches of China Construction Bank in Anhui Province is employed to illustrate the proposed approach.     

基于竞争决策单元的数据包络分析模型

决策单元之间处于竞争关系时,所有决策单元的某些投入或产出总和是有限的,一个决策单元改善自身效率的行为可能会影响其他决策单元.针对此情形,提出一种基于竞争决策单元的数据包络分析模型,比较了该模型与传统DEA模型的生产前沿面的区别,并指出此模型在管理决策领域可能的用途.最后,用一个算例分析演示了此模型.     

DEA模型的一般投影研究

首先探讨了ＤＥＡ模型的一般投影模型及性质，为非ＤＥＡ有效的决策单元提供了多种改进方案，并在此基础上提出可控投影模型，从而能得到适合决策单元自身状况的调整方案，为决策者提供更有价值的参考信息     

基于非径向超效率DEA聚类模型的 FEEEP系统协调发展

在传统DEA理论的基础上构建了非径向超效率DEA聚类模型,并将该模型和传统DEA模型对比分析了我国各地区FEEEP系统的协调发展水平.结果表明: 和传统DEA模型相比, 该模型弥补了传统DEA模型的缺陷,能有效地区分生产前沿面上FEEEP系统的协调发展水平,模型的排序结果能准确地反映各地区FEEEP系统的协调发展实际水平,为区域可持续发展政策的制定提供了科学的决策依据.     

基于误差传递和熵的区间DEA方法

传统的DEA(data envelopment analysis)模型只能用来处理精确数据,而在现实生活中,由于统计误差、信息不完全等原因,获取精确数据十分困难,数据往往只能以区间数的形式给出.因此,如何在不确定条件下利用DEA方法评价决策单元的相对效率一直是DEA理论的研究前沿.针对这一问题,文章在DEA传统模型的基础上,利用改进的交叉效率思想,使用误差传递和熵处理区间数据,求得每个决策单元全局交叉效率的误差分布形式(中点值和误差最大估计值),然后根据全局交叉效率,使用有向距离指数给决策单元排序.最后通过两个算例来说明本文提出的方法的可行性和有效性.     

Measuring Environmental Performance of Provincial Thermal Power Plants in China: A Malmquist DEA Approach with Fixed-Sum Undesirable Outputs

With the development of China’s economy, environmental pollution has become cumulatively serious. The primary source of environmental pollution is thermal power generation, which has attracted the attention of governments and academia in recent years. To effectively reduce environmental pollution, research should study how to constrain the undesirable output of thermal power plants, that is, to limit the total undesirable output of the plants to a certain fixed sum. However, few studies have suggested that these undesirable outputs should be fixed-sum outputs. Moreover, no previous research publication about thermal power plants has analyzed their environmental performance changes. To address these gaps, a novel Malmquist-DEA approach is proposed for evaluate the environmental performance of thermal power plants in this paper. This approach generalizes the equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and incorporates the model into the Malmquist productivity index (MPI). The authors apply this approach to the analysis of provincial thermal power plant environmental performance in China and analyze such plants’ trends based on panel data from 2011 to 2014. The empirical research shows that the environmental performance of regional thermal power plants was positively affected by efficiency change and negatively affected by technical change. Finally, the authors provide policy suggestions based on our findings.

     

用于广义DEA有效性的度量方法

在广义数据包络分析方法(data envelopment analysis,DEA)有效性度量方面还存在以下两个问题有待解决：第一,当广义DEA模型无可行解时,尽管被评价单元本身是有效的,但却无法用具体数值刻画其有效程度;第二,当单元为广义DEA有效时,某些单元的效率值存在被高估的情况。为解决这些问题,首先给出一个拓展的广义DEA模型。然后,给出了一种判定广义DEA模型存在无可行解情况的充要条件,以及单元效率被高估的判定条件。同时给出测定单元效率的3种方法,并讨论这些方法与原有测定方法的关系。最后,通过实际例子进行了对比研究。     

综合网络DEA模型有效性理论分析

针对综合DEA模型无法反映系统内部子过程的相互作用对绩效的影响,提出一种综合网络DEA模型,并给出该混合模型的相关定义及定理,得到综合网络DEA模型由参数取值不同,各(弱)网络DEA有效性间的关系;指出了综合网络DEA有效的决策单元,也为评价决策单元总体有效性的综合DEA有效,同时该单元内部子过程也均为综合DEA有效;证明决策单元为(弱)综合网络DEA有效时,评价决策单元总体有效性的综合DEA最优解与相应综合多目标规划的(弱)Pareto解之间的对应关系。这些结果进一步丰富了DEA有效性理论。     

A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS

The traditional data envelopment analysis(DEA)model can evaluate the relative efficiencies of a set of decision making units(DMUs)with exact values of inputs and outputs,but it cannot handle imprecise data.Imprecise data,for example,can be expressed in the form of the interval data or mixtures of interval data and ordinal data.In this study,a cross-efficiency method is introduced into the DEA model to calculate the interval of cross-efficiency values,based on which a new TOPSIS method is proposed to rank the DMUs.Two examples are presented to illustrate and validate the proposed method.