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.     

以多输入为决策变量的DEA模型的解法

给出了以多输入为决策变量的DEA模型的解法.     

强DEA有效性的探讨

给出了有关强DEA有效(C2R或C2GS2)的一些必要条件,判断方法及等价的命题,特别是给出了其存在性定理、及有关强DEA有效与扩展DEA有效等价的定理.     

基于PCA和DEA方法的北京市可持续发展能力的评价研究

利用主成分分析法,以2000-2009年数据为基础得出了北京市可持续发展系统中的六类成本和效益性综合指数,依此从投入产出角度建立DEA评价模型,并通过模型中指标变量的调整,对北京市近10年可持续发展系统的总体水平进行了评价分析。得出北京具有可持续发展的基础和能力,但北京在可持续性发展中必须注重优化投入产出结构,提高资源与环境的利用率,搞好循环经济。     

指标可取负值的基于输入与输出的DEA模型

有关基于输入与输出的DEA模型,本文与现有文献的不同之处,一是模型中的评价指标可取负值,二是被评的决策单元可以不是所给的n个决策单元之一,三是模型并非由多目标规划模型推得.此外,给出了有关此模型的三个定理.因此,可知有关此模型的最优解存在的充分条件;在求解此模型后就能在判断决策单元的DEA有效性的同时计算出其相对效率,并能计算出其在DEA相对有效面上的"投影".     

政府网站评估DEA模型

本文运用DEA（Data Envelopment Analysis）技术，建立了电子政务网站评估的DEA模型，并对全国28个省市的政府网站进行了详细的评估。该评估模型与目前常用的电子政务网站评估方法不同之处在于，不仅比较各网站的物理表征指标与服务内涵，还考虑到各省市的社会、经济发展基本指标。这样将电子政务的活动同其建设环境联系起来，更客观地评估人在电子政务中的能动作用，以及电子政府作为建设项目的投人产出效率。同时，利用DEA分析方法进行评价，还能向各网站提供如何改进网站的建设工作的指导信息，为有关部门对政府网站的评价和管理提供了一种新方法。     

基于改进DEA模型的科技投入产出有效性分析

合理地评价各地区的科技投入产出,对于资源的合理利用,提高资金的使用效率具有十分重要的现实意义.采用一种改进的DEA模型(简称M DEA),对2000—2002年间我国各地区的科技投入产出相对效率进行了充分评价和排序.并将三年的数据放在一起组成一个新的参考集,用同样的M DEA模型进行评估,得出各地区三年科技投入产出相对效率的变化情况.     

扩展的逆DEA问题

扩展的逆DEA问题与逆DEA问题的区别在于变化后的被评决策单元的效率指数为给定的数p(0相似文献   

基于DEA的物流配送中心再选址研究

为实现现有物流配送中心的优化管理的目的,利用DEA方法对已有配送中心的效率进行评估.根据评估的结果代入选址分析模型中,得到物流配送中心再选址的DEA评估规划模型,以配送收益与物流成本之差作为目标函数,以供需均衡关系、配送中心流出量与流入量平衡、选址变量与流量内在联系构建约束条件,通过算例并借助于MATLAB软件验证了模型的有效性.     

有关DEA中评价与排序的新方法的探讨

用新方法讨论了DEA中对决策单元的评价与排序,并研究了一些新问题.     

Scaling units via the canonical correlation analysis in the DEA context

This paper deals with the evaluation of decision making units which have multiple inputs and outputs. A new method (CCA/DEA) is developed where the Canonical Correlation Analysis (CCA) is utilized to provide a full rank scaling for all the units rather than a categorical classification (for efficient and inefficient units) as done by the Data Envelopment Analysis (DEA). The CCA/DEA approach is an attempt to bridge the gap between the frontier approach of DEA and the average tendencies of statistics (econometrics). Nonparametric statistical tests are employed to validate the consistency between the classification from the DEA and the postclassification that was generated by the CCA/DEA.     

Dealing with interval scale data in data envelopment analysis

This paper considers the problem of interval scale data in the most widely used models of data envelopment analysis (DEA), the CCR and BCC models. Radial models require inputs and outputs measured on the ratio scale. Our focus is on how to deal with interval scale variables especially when the interval scale variable is a difference of two ratio scale variables like profit or the decrease/increase in bank accounts. We suggest the use of these ratio scale variables in a radial DEA model.     

DEA and the discriminant analysis of ratios for ranking units

The purpose of this study is to develop a new method which provides for given inputs and outputs the best common weights for all the units that discriminate optimally between the efficient and inefficient units as pregiven by the Data Envelopment Analysis (DEA), in order to rank all the units on the same scale. This new method, Discriminant Data Envelopment Analysis of Ratios (DR/DEA), presents a further post-optimality analysis of DEA for organizational units when their multiple inputs and outputs are given. We construct the ratio between the composite output and the composite input, where their common weights are computed by a new non-linear optimization of goodness of separation between the two pregiven groups. A practical use of DR/DEA is that the common weights may be utilized for ranking the units on a unified scale. DR/DEA is a new use of a two-group discriminant criterion that has been presented here for ratios, rather than the traditional discriminant analysis which applies to a linear function. Moreover, non-parametric statistical tests are employed to verify the consistency between the classification from DEA (efficient and inefficient units) and the post-classification as generated by DR/DEA.     

DEA models for ratio data: Convexity consideration

Data envelopment analysis (DEA) is defined based on observed units and by finding the distance of each unit to the border of estimated production possibility set (PPS). The convexity is one of the underlying assumptions of the PPS. This paper shows some difficulties of using standard DEA models in the presence of input-ratios and/or output-ratios. The paper defines a new convexity assumption when data includes a ratio variable. Then it proposes a series of modified DEA models which are capable to rectify this problem.     

A comparison of chance-constrained DEA and stochastic frontier analysis: bank efficiency in Taiwan

We employed both chance-constrained data envelopment analysis (CCDEA) and stochastic frontier analysis (SFA) to measure the technical efficiency of 39 banks in Taiwan. Estimated results show that there are significant differences in efficiency scores between chance-constrained DEA and stochastic frontier production function. The advanced setting of the chance-constrained mechanism of DEA does not change the instinctive differences between DEA and SFA approaches. We further find that the ownership variable is still a significant variable to explain the technical efficiency in Taiwan, irrespective of whether a DEA, CCDEA or SFA approach is used.     

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 .     

Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA)

This note is concerned with joint uses of imprecise data and assurance regions (ARs) in data envelopment analysis (DEA), referred to as assurance region-imprecise DEA (AR-IDEA). It has been developed to transform the AR-IDEA models into ordinary linear programming equivalents via scale transformations and variable alterations plus introducing dummy variables. In this note, we show one simpler approach for achieving linear programming equivalents only by variable alterations without rescaling as well as introducing dummy variables. We also provide findings in the use of imprecise data and AR conditions in DEA. This points out that some AR conditions do not affect the efficiency ratings under AR-IDEA.     

Experimental evidence on robustness of data envelopment analysis

There is an on-going debate about variable selection in data envelopment analysis (DEA) as there are no diagnostic checks for model misspecification. This paper contributes to this debate by investigating the sensitivity of DEA efficiency estimates to including inappropriate and/or omitting several important variables in a large-sample DEA model. Data are simulated from constant, increasing and decreasing returns-to-scale (RS) Cobb–Douglas production processes. For constant and decreasing RS processes with irrelevant inputs, DEA tends to overestimate efficiency in almost all production units. When relevant variables are omitted, variable RS appears to be a safer option. The correct RS specification is vital when the DEA model includes irrelevant variables. The effect of omission of relevant inputs on individual production unit efficiency is more adverse compared to the inclusion of irrelevant ones.     

Methods of critical value reduction for type-2 fuzzy variables and their applications

A type-2 fuzzy variable is a map from a fuzzy possibility space to the real number space; it is an appropriate tool for describing type-2 fuzziness. This paper first presents three kinds of critical values (CVs) for a regular fuzzy variable (RFV), and proposes three novel methods of reduction for a type-2 fuzzy variable. Secondly, this paper applies the reduction methods to data envelopment analysis (DEA) models with type-2 fuzzy inputs and outputs, and develops a new class of generalized credibility DEA models. According to the properties of generalized credibility, when the inputs and outputs are mutually independent type-2 triangular fuzzy variables, we can turn the proposed fuzzy DEA model into its equivalent parametric programming problem, in which the parameters can be used to characterize the degree of uncertainty about type-2 fuzziness. For any given parameters, the parametric programming model becomes a linear programming one that can be solved using standard optimization solvers. Finally, one numerical example is provided to illustrate the modeling idea and the efficiency of the proposed DEA model.     

Anchor points in DEA

Anchor points play an important role in DEA theory and application. They define the transition from the efficient frontier to the “free-disposability” portion of the boundary. Our objective is to use the geometrical properties of anchor points to design and test an algorithm for their identification. We focus on the variable returns to scale production possibility set; our results do not depend on any particular DEA LP formulation, primal/dual form or orientation. Tests on real and artificial data lead to unexpected insights into their role in the geometry of the DEA production possibility set.