二阶段加法DEA模型的生产可能集与生产前沿面（英文）

针对二阶段加法DEA模型的中间要素的特殊性,构造生产可能集及其公理体系,由此定义生产前沿面,并建立DEA有效和生产前沿面之间的等价关系.通过构造一个多目标规划模型,建立该问题的Pareto有效解与DEA有效之间的等价关系.     

只有产出(投入)BC~2模型中决策单元效率的几何刻画

在DEA方法中,DEA有效和弱DEA有效的决策单元位于生产前沿面上,非弱DEA有效的DEA无效决策单元位于生产可能集的内部而非生产前沿面上.通过引入生产可能集与生产前沿面移动的思想,证明只有产出(投入)的BC²模型评价下的决策单元的最优值与相应的生产前沿面的移动值存在倒数关系,以双产出(投入)情形图示说明,明确了决策单元在生产可能集中所处的位置.     

基于生产前沿面的DEA有效单元再评价研究

对基于生产前沿面的DEA有效单元评价方法进行了研究.在分析总结当前基于优势前沿面和基于临界前沿面两种方法优缺点的基础上,提出了基于劣势前沿面的评价方法.新方法在分辨率和可比性方面进行了改进.     

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。     

生产函数的规模有效性和综合DEA模型C~2WY

本文运用DEA模型C~2WY证明了生产函数y=■(x)的规模有效性就是DEA有效。     

用DEA方法确定生产函数

本文指出,采用DEA方法确定生产函数的优越性;证明了DEA有效面即是有效生产前沿面,并就一特殊情况作了详细讨论。指出DEA有效面是对实际生产函数的一种逼近;最后,介绍DEA方法在生产函数研究中的应用进展。     

生产函数与综合 DEA 模型 C~2WY

DEA(数据包络分析)方法是一种新的决策方法,它可以用来评价决策单元之间的相对有效性.从生产函数的角度看,这是用来研究生产部门“规模有效”和“技术有效”性的一种卓有成效的方法.在经济领域中,用其确定相对的有效生产前沿面时得到了充分的应用.1978年,美国著名运筹学家 Charnes,Cooper 及 Rhodes 提出了关于生产部门同时为“规模有效”与“技术有效”的 C~2R 模型,这是 DEA 方法的第一个模型;1985年     

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.     

具有偏好锥的链式网络DEA模型

对链式网络DEA模型进行推广,将"偏好锥"引入网络DEA模型.针对中间产出重要性以及决策者评价时的偏好,建立带有产出锥和投入锥相应的两阶段生产可能集,对具有"偏好锥"的链式网络DEA模型,证明了决策单元为网络DEA有效的充要条件,给出了网络DEA有效性与各阶段弱DEA有效性的关系.另外,文章结合具体算例说明了偏好锥的变化对效率评价的影响.关于两阶段的模型以及相关结论可以推广到多阶段网络结构.     

沿法线方向改进非DEA有效DMU

现有的非DEA有效DMU的改进方法造成DMU的投入或产出的波动太大,因而难以进行改进.提出了沿法线方向改进非DEA有效DMU的新方法.可以使非DEA有效DMU尽快到达有效前沿面,成为DEA有效,减小了波动幅度,并结合12所重点理工高校效评价的实际,验证了本方法的优势.     

Deriving the DEA frontier for two-stage processes

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides not only an overall efficiency score for the entire process, but as well yields an efficiency score for each of the individual stages. Due to the existence of intermediate measures, the usual procedure of adjusting the inputs or outputs by the efficiency scores, as in the standard DEA approach, does not necessarily yield a frontier projection. The current paper develops an approach for determining the frontier points for inefficient DMUs within the framework of two-stage DEA.     

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.     

A bargaining game model for measuring performance of two-stage network structures

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model not only provides an overall efficiency score for the entire process, but also yields an efficiency score for each of the individual stages. The current paper develops a Nash bargaining game model to measure the performance of DMUs that have a two-stage structure. Under Nash bargaining theory, the two stages are viewed as players and the DEA efficiency model is a cooperative game model. It is shown that when only one intermediate measure exists between the two stages, our newly developed Nash bargaining game approach yields the same results as applying the standard DEA approach to each stage separately. Two real world data sets are used to demonstrate our bargaining game model.     

Incorporating performance measures with target levels in data envelopment analysis

Data envelopment analysis (DEA) is a technique for evaluating relative efficiencies of peer decision making units (DMUs) which have multiple performance measures. These performance measures have to be classified as either inputs or outputs in DEA. DEA assumes that higher output levels and/or lower input levels indicate better performance. This study is motivated by the fact that there are performance measures (or factors) that cannot be classified as an input or output, because they have target levels with which all DMUs strive to achieve in order to attain the best practice, and any deviations from the target levels are not desirable and may indicate inefficiency. We show how such performance measures with target levels can be incorporated in DEA. We formulate a new production possibility set by extending the standard DEA production possibility set under variable returns-to-scale assumption based on a set of axiomatic properties postulated to suit the case of targeted factors. We develop three efficiency measures by extending the standard radial, slacks-based, and Nerlove–Luenberger measures. We illustrate the proposed model and efficiency measures by applying them to the efficiency evaluation of 36 US universities.     

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.     

Measuring the efficiency of forest districts with multiple working circles

Data envelopment analysis (DEA) is a methodology extensively applied to measuring the relative efficiency of decision making units with multiple inputs and multiple outputs. Herein, a DEA model is developed to measure the efficiency of forest districts which are divided into a number of subdistricts called working circles (WCs). The idea is to construct district production frontiers from the WCs of individual districts. Superimposing the district production frontiers of different districts one derives the forest production frontier. The closeness of a district production frontier to the forest production frontier indicates this district's efficiency. As an illustration, the developed model measures the eight districts, with a total of thirty-four WCs, of the national forests of the Republic of China on Taiwan. The results provide the top management with an idea of how far each district can be expected to improve its performance when compared with other districts.     

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.     

Convexity,quality and efficiency in education

While Data Envelopment Analysis (DEA) has many attractions as a technique for analysing the efficiency of educational organisations, such as schools and universities, care must be taken in its use whenever its assumption of convexity of the prevailing technology and associated production possibility set may not hold. In particular, if the convexity assumption does not hold, DEA may overstate the scope for improvements in technical efficiency through proportional increases in all educational outputs and understate the importance of improvements in allocative efficiency from changing the educational output mix. The paper therefore examines conditions under which the convexity assumption is not guaranteed, particularly when the performance evaluation includes measures related to the assessed quality of the educational outputs. Under such conditions, there is a need to deploy other educational efficiency assessment tools, including an alternative non-parametric output-orientated technique and a more explicit valuation function for educational outputs, in order to estimate the shape of the efficiency frontier and both technical and allocative efficiency.