评价相对效率的投入-产出型DEA

传统DEA只能在固定投入或产出的条件下,从产出或投入角度测算决策单元相对效率,因而不能综合地反映决策单元的投入产出效果.基于双目标规划,本文将提出从投入及产出角度评价决策单元相对效率的投入-产出型DEA,并研究其相对有效性.最后以沪市16家高科技上市公司为应用实例,研究其相对经营效率.     

基于双准则规划的DEA及其相对效率

传统的ＤＥＡ通过事先假定投入和产出之一不变,而将另一个要素尽量缩小或扩大,并用最大的扩大比率或投入缩小比率来描述决策单元的相对最高效率。     

基于分段参数型DEA前沿面的投入—产出型技术效率

由传统DEA模型可以直接测算投入固定(产出固定)的条件下,面向产出(投入)的技术效率。尽管加型DEA模型同时考虑了投入和产出的松弛,但却不能像传统模型一样直接测算投入—产出型技术效率。为了直接由加性模型测算投入产出型技术效率,本文将利用DEA有效决策单元建立分段参数型DEA生产前沿面,并根据古典技术效率的定义,解决投入产出型技术效率的测算问题。研究发现,这种效率实质上是产出型技术效率与投入配置效率的乘积。由于同时考虑了投入和产出的技术无效性,与其它类型的技术效率相比,这种投入产出型技术效率的可分性更强。     

基于混合DEA模型的医药行业上市公司经营效率相对有效性评价

DEA模型作为一种客观的多指标决策方法应用十分广泛.但目前使用频率较高的两种通用模型C²R模型和C²GS²模型,在单独使用时都有比较大的局限性,评判结果往往无法令人满意;针对这一问题,本文提出构建混合DEA模型,并应用混合DEA模型对医药上市公司的经营效率相对有效性评价进行实证分析,阐述了混合DEA模型的使用方法,验证了评判方法的可行性和科学性.     

基于DEA模型下的科技投入产出效率评价研究——以广东省科技投入产出效率评价为例

通过构建科技投入产出指标体系,运用DEA模型对广东省2000-2012年的科技投入产出效率进行评价。结果表明:广东省的科技投入产出效率平均值达0.962,科技资源配置基本处于相对最佳状态,不存在科技资源过度浪费的现象,科技产出的不足在科技投入不断合理、高效利用的过程中逐渐得到消除。广东省应在有限的科技人力、财力资源条件下进一步提高资源的利用效率;在有条件的情况下,适当增加科技投入,以期获得更多的科技产出。     

基于交叉评价策略的DEA全局协调相对效率排序模型

在DEA(数据包络分析)研究领域,建构在交叉效率概念基础上的现有决策单元排序方法仅以定义的方式给出了用于决策单元排序的交叉效率评价值。对于这种方法构建方式,分别基于管理学的效率概念和多属性决策理论,分析指出其中的交叉效率评价值从本质上讲既与效率的管理学概念不符,也与决策单元的优劣不存在理性逻辑联系。为克服现有决策单元排序方法所存在的上述问题,基于交叉评价策略和效率的管理学概念内涵给出了DEA全局协调相对效率的新概念,在此基础上利用优化理论给出了可以用于决策单元优劣排序的DEA全局协调相对效率测度模型,并通过理论分析和数值案例验证解释了该模型相对于现有决策单元排序方法所拥有的比较优势。     

基于模糊DEA的循环经济效益评价

伴随着经济的高速增长,环境逐渐恶化,资源浪费问题日益突出,使得提倡“资源节约型、环境友好型”的循环经济成为近年来人们关注的热点。本论文选取西部地区十一个省作为研究对象,运用模糊DEA模型分析评价了其循环经济的相对有效性,并提出了各省循环经济的改进意见。     

只有输出的广义样本区间DEA模型

在应用多个绩效指标综合评价决策单元有效性时，决策者常常把这些决策单元与另外预先指定的标准（样本单元）进行比较。由于客观事物的复杂性和不确定性导致样本单元和决策单元的指标信息有时必须用区间数的形式给出。针对区间数指标信息的综合评价问题，本文通过分解的方法讨论样本单元和决策单元指标信息为区间数时用广义DEA模型评价决策单元有效性的方法，并相应地构建了只有输出的广义区间DEA模型。同时，对模型的含义、求解以及性质等进行了分析。之后，探讨了该方法在决策单元有效性分类和排序中的应用。最后，通过实例表明该方法的可行性和有效性。     

基于DEA的房地产业投入效率分析

本文采用数据包络法评价2003~2012年中国35个大中城市房地产业的投入效率。研究表明:我国房地产业投入效率整体偏低,大部分城市房地产业投入出现DEA无效的情况。严格控制土地供应、合理配置房地产投资规模和完善住房保障体系是提高房地产业投入效率的重要途径。     

并联决策单元的动态DEA效率评价研究

具有并联决策单元内部结构的系统是一类具有代表性的复杂生产系统,其内部结构已不再是传统观念的黑箱,而是由多个并联(平行)的生产单位组成.在已有并联决策单元DEA效率评价方法的基础上加入时间维度,增加规划问题的约束条件,推演出该种复杂系统的动态DEA效率评价指标,并以中国14家商业银行的数据进行实证.研究结果表明,2005年至2008年没有一家银行的动态效率指数为0,这说明中国银行系统的生产效率并没有达到最优;中国商业银行的地区效率基本遵循沿海地区-中部地区-西部地区依次递减的态势,此规律在中国国有银行中的体现更为明显;影响银行效率改进的主要原因是各银行本身的差异,而不是不同经济区域之间的环境差别.     

一种新的双目标DEA模型

在经济系统的分析研究中,由于资源的有限性,我们总想以最少的投入获得最多的产出。本文给出了决策单元输入总量最小且输出总量最大时的一种双准则DEA模型,并用线性加权和法对其进行了求解,该模型能使所有决策单元同时达到相对效率最优。     

基于理想决策单元参照求解策略的DEA交叉效率评价模型

在已有关于DEA交叉效率评价模型中,激进型模型和仁慈型模型会因评价结果不一致而导致实际应用中难以对它们予以抉择的难题;中立型模型虽在形式上规避了前述问题,但其本身存在着理论偏差。针对上述问题,基于TOPSIS的理想点构造方法,提出了一种关于DEA交叉效率评价的新模型,即基于理想决策单元参照求解策略的DEA交叉效率评价模型。该模型不仅具有理论的严谨性,可以规避激进型模型与仁慈型模型之间的选择难题,而且相对于它们而言能够更好地坚持DEA最有利于被评价决策单元的基本思想。数值模拟分析表明新模型具有解决实际问题的较好适用性。     

Risk management and managerial efficiency in Chinese banks: A network DEA framework

Risk management in Chinese banks has traditionally been the Cinderella of its internal functions. Political stricture and developmental imperative have often overridden standard practice of risk management resulting in large non-performing loan (NPL) ratios. The training and practice of risk managers remain second class compared with foreign banks operating in China. This paper surveys Chinese bank risk managers and constructs metrics of risk management practice and risk management organisation. The metrics are used as intermediate inputs in a Network DEA framework to produce a measure of income efficiency. A statistical test is carried out to assess the importance of the risk metrics in evaluating bank income efficiency.     

矩阵型网络DEA模型及其实证检验

针对传统DEA模型无法有效的评价矩阵型网络系统的效率,本文构建了矩阵型网络决策单元的生产可能集,建立了矩阵型网络DEA模型。在此基础上证明了决策单元在矩阵型网络DEA模型下为弱DEA有效的充分必要条件为其每个子系统均为弱DEA有效。最后,选用美国的十个电力公司作为决策单元对模型进行实证检验,得出结论:矩阵型网络DEA模型弥补了传统DEA模型无法反映内部有效性从而可能得到错误结果的缺陷,并能精确地计算出各个子过程的效率,辨识出具体需要改进的子过程。同时新模型为评价复杂系统的效率提供了新的思路。     

Goal directed benchmarking for organizational efficiency

In this paper, we extend the standard data envelopment analysis (DEA) model to include longer term top management goals. This extension is in recognition of the fact that benchmarking for decision making units (DMUs) is more than a purely monitoring process, and includes a component of future planning. The new model uses a goal programming structure to find points on the efficient frontier which are realistically achievable by DMUs, but at the same time achieving a closer approach to long term organizational goals (as distinct from the local performance of individual DMUs). Consideration is given to the possibility of adjusting constraints on the DMU by investment in extended inputs or new technologies, in which case minimization of associated investment costs becomes an additional management objective.     

Estimation of portfolio efficiency via DEA

Traditional DEA models and nonlinear (diversification) DEA models are often used in performance evaluation of portfolios. However, the diversification models are usually very complicated to compute except the very basic models. And the classic DEA models still need to be further justified and tested, since it is not clear whether they are over-linearised according to the diversification models. The existing studies on performance evaluation via the classic DEA models generally focus on the selection of inputs and outputs. In this work, we investigate theoretical foundation of DEA models for portfolios from a perspective of sampling portfolio. We show the classic DEA provides an effective and practical way to approximate the portfolio efficiency (PE). We further verify this approach through different portfolio models with various frictions and bounds on the market. Through the comprehensive simulations carried out in this study, we show that with adequate data sets, the classic DEA models can effectively sample portfolios to take into account sufficient diversification, and thus can be used as an effective tool in computing the PE for their performance assessments. This study can be viewed as a justification of the classic DEA performance assessments and the way to introduce other efficiency notions (like allocation efficiency, scale efficiency, etc) into assessment of portfolios.     

DEA with streaming data

DEA can be interpreted as a tool for the identification of “frontier outliers” among data points. These are points that are potentially interesting because they exhibit extreme properties in that the values of their attributes, either alone or combined, are at the upper or lower limits of the data set to which they belong. A real challenge for this type of frontier analysis arises when data stream in at high rates and the DEA analysis needs to be performed quickly. This paper extends DEA into this dynamic data environment. The purpose is to propose a formal theoretical framework to handle streaming data and to answer the question of how fast data can be processed using this new framework. Potential applications involving large data sets include auditing, appraisals, fraud detection, and security. In such settings the situation is likely to be dynamic with the data domain constantly changing as new entities arrive in the course of time. New specialized tools to adapt DEA to deal with streaming data will be explored.     

A modified DEA model to estimate the importance of objectives with an application to agricultural economics

This paper demonstrates a connection between data envelopment analysis (DEA) and a non-interactive elicitation method to estimate the weights of objectives for decision-makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows us to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of multiple attribute decision analysis the basic contribution of this paper is a new interpretation in terms of efficiency of the non-interactive methodology employed to estimate weights in a multicriteria approach. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures that does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.