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.     

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 1 6家高科技上市公司为应用实例 ,研究其相对经营效率     

基于DEA模型的商业银行效率评价研究

DEA模型是一种有效地处理多投入多产出的效率评价模型。运用DEA模型对商业银行进行效率评价能够综合地考察商业银行经营管理情况,并通过选择合适的投入产出指标,直接指明与最佳银行相比,被评价银行在哪些投入产出项目上有差距,从而可以找出改进被评价银行经营效率的最佳途径。     

随机环境下的多投入多产出生产前沿面估计

传统的DEA模型假设观测样本的投入产出都是确定型数据, 这使得DEA在实际应用中受到限制, 本文提出的基于拟似然估计的多投入多产出随机非参数包络数据 (PLE-StoNED) 方法拓展了这个假设, 能够估计随机环境下的生产前沿面.本文证明, 生产可能集假设条件下的前沿面可以用一个有凹凸性和单调性限制的函数来表示.相较之前的StoNED方法, 本文提出的方法可以估计随机环境下多投入多产出决策单元 (DMU) 的前沿面.通过Monte Carlo实验, 多投入多产出PLE-StoNED方法的有效性得到验证, 它可纠正DEA等传统方法产生的偏误.最后, 实证研究部分运用这一新提出的方法估计了中国大陆商业银行的生产前沿面和效率.本文提出的方法弥补了DEA缺乏统计性的不足, 可为决策者在随机环境下对多投入多产出决策单元进行生产力和效率评估提供决策参考.     

基于滞后DEA的我国高校科研经费使用效率评价

研究我国高校科研经费使用效率。基于2002-2006年31所高校的截面数据分析,运用统计方法建立了高校科研经费使用效率评价指标体系,测算了科研产出滞后于投入的时间,建立了滞后DEA效率评价模型,从投入产出角度动态分析了我国31所代表性高校科研经费相对使用效率,并对非有效的高校进行了规模有效性和投影分析计算,得出了我国高校科研经费使用特点和存在的问题。结果表明我国高校科研经费使用效率整体不高,48%的高校存在一定程度的投入不足和产出剩余问题,最后针对存的在问题提出了改进建议。     

重庆市可持续发展能力研究

从投入产出角度出发结合可持续发展的指标建立了DEA(数据包络分析)模型,分析了在1997年重庆从四川省分割出去以后重庆的发展轨迹。发现重庆市在分割出去后的10年间可持续发展能力都逐渐增强,而技术无效性是影响它可持续发展能力的主要因素。分别对非DEA有效的年份进行进一步分析,通过计算各指标投入冗余、产出不足,指出以后改进的方向。     

一个新的考虑非期望产出的非径向-双目标DEA模型

如何实现对环境效率更准确的定量评价,是国际上从事DEA研究的学者们当前关注的问题之一。在传统DEA效率模型的基础上,综合考虑评价过程中期望产出与非期望产出之间的差异以及实际生产过程中人们追求期望产出最大化和非期望产出最小化的双重目标,构建非期望产出的非径向-双目标DEA环境效率评价模型,通过线性加权和法转化为一个求最大值的单目标线性规划问题。研究结果表明,这种新模型不仅可以分析DEA有效性与Pa-reto最优之间的关系,还利用决策单元的投影获得投入和产出的可调整量,从而提高考虑非期望产出的环境效率评价的精度。实证分析结果与现实情况的高度吻合,说明这种新的非径向-双目标DEA环境效率模型是有效的。     

基于DEA方法的信贷资金区域配置效率评价——以浙江省11市为例

本文运用数据包络模型(DEA),分别选取贷款余额和银行业从业人数作为投入指标,国内生产总值作为产出指标,对浙江省11地市2004～2009年间信贷资金区域配置相对效率进行测算。研究发现,浙江信贷资金区域配置效率呈现出较为明显的"马太效应",且相对于整体较高的纯技术效率,较低的规模效率是限制11市综合效率提升的主因。     

并联系统资源和目标配置双准则DEA模型

针对决策单元内部资源和目标配置问题,以并联生产系统为研究对象,研究数据包络分析方法在组织的资源分配与目标设定方面的应用.首先考虑决策单元内部评价的特点:(1)固定投入和固定产出;(2)子单元评价的权重选择之间的关系;(3)内部评价权重选择与决策单元整体评价的关系.在此基础上,提出基于效率与公平的决策单元内部资源和目标配置双准则DEA模型,最后用算例演示其使用,以说明模型的合理性.     

基于两阶段交叉效率模型的中国商业银行效率评价

为了更好的评价我国商业银行的效率,在传统两阶段DEA模型中引入交叉效率,并对两阶段生产系统的内部结构进行优化,构建了考虑共享投入和非期望产出的两阶段交叉效率模型。最后结合中国16家商业银行2006-2015年的年度报表,分别应用传统的两阶段DEA模型和新模型评价样本银行的效率。结果表明：新模型比传统模型更客观、区分力更强,具有更强的稳定性,更适合用于评价我国商业银行的效率。     

环保项目有效性评价的数据包络分析方法

利用数据包络分析(DEA)方法评价环保项目有效性时,输入、输出数据中可能同时存在环境因素,不符合传统的DEA模型要求,需要对这些数据进行转换。本文讨论了"不好的"数据平移转换法、输入和输出因素转换法、倒数转换法,建立了三种DEA模型,对建立的DEA模型一致性进行了分析,拓宽了以前DEA模型的应用范围。一个例子验证了这三种方法在评价环保项目有效性时是一致的。     

基于修正Russell方法的模糊决策单元的排序

文章首先建立基于修正Russell方法的超效率DEA模型,然后基于模糊数的比较,建立并求解模糊环境下的基于修正Russell方法的超效率DEA模型,从而解决了模糊决策单元的全排序问题。文末的算例将基于修正Russell方法的模糊超效率DEA模型,与基于CCR模型的模糊超效率DEA模型的结果进行了比较分析。     

Two-stage DEA models with undesirable input-intermediate-outputs

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still lacks of more systematical investigation on two-stage DEA with undesirable variables. For instance, depending on its operating model, even whether an intermediate variable is desirable or undesirable can be questionable for a particular two-stage system. Furthermore, most of the existing studies on two-stage systems focus on the case where only the final outputs are undesirable. In this work, we try to systematically examine two-stage DEA models with undesirable input-intermediate-outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets (PPS) and the corresponding DEA models with undesirable variables. The proposed models are then used to illustrate some theoretical perspectives by using the data of China׳s listed banks.     

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.     

Multi-output profit efficiency and directional distance functions

We extend a recently developed DEA methodology for cost efficiency analysis towards profit efficiency settings. This establishes a novel DEA toolkit for profit efficiency assessments in situations with multiple inputs and multiple outputs. A distinguishing feature of our methodology is that it assumes output-specific production technologies. In addition, the methodology accounts for the use of joint inputs, and explicitly includes information on the allocation of inputs to individual outputs. We also establish a dual relationship between our multi-output profit inefficiency measure and a technical inefficiency measure that takes the form of a multi-output directional distance function. Finally, we demonstrate the empirical usefulness of our methodology by an empirical application to a large service company.     

Measuring performance of two-stage network structures by DEA: A review and future perspective

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to the second stage. The current paper reviews these models and establishes relations among various approaches. We show that all the existing approaches can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. Future perspectives and challenges are discussed.     

存在保证域的模糊非径向偏好DEA模型——基于中科院24个研究所的实证分析

数据包络分析（DEA）是一种非参数化的方法,用于评价具有类似输入和输出的决策单元的效率。传统的非径向DEA模型假设输入和输出数据均为准确值,且对权重变量不加以限制,本文构建了存在保证域的模糊非径向偏好DEA模型,并给出了一种基于模糊数截集的模型求解方法,有效地解决了输入和输出全部或部分为模糊数的决策单元评价问题。最后给出了一个中科院研究所效率评价的实例说明了方法的有效性。     

DEA决策单元的广义价值效率测度模型

为了克服DEA模型存在权重取值过于灵活的缺陷,在DEA建模原理的基础上,通过引入价值偏好估计对经典DEA模型予以了科学推广,并依据该模型推广建立了能够适用于决策者价值函数为非线性函数情况的DEA权重置信域构造方法与相应的决策单元价值效率分析方法即DEA广义价值效率测度实用模型。该模型的主要优点在于:权重置信域的构造具有科学严密性和价值信息引入的可操作性,从而可以保证分析结论的可靠性。数值验证结果表明,该模型可以得出可靠、可信且具有较强分辨能力的价值效率评价结论。     

Measuring efficiency with products,by-products and parent-offspring relations: A conditional two-stage DEA model

In the usual data envelopment analysis (DEA) setting, as pioneered by Charnes et al. (1978) [1], it is assumed that a set of decision making units (DMUs) is to be evaluated in terms of their relative efficiencies in converting a bundle of inputs into a bundle of outputs. The usual assumption in DEA is that each output is impacted by each and every member of the input set. One particular area of recent research is that relating to partial input to output impacts where the main issue addressed is that in many settings not all inputs impact all outputs. In that situation the authors view the DMU as consisting of a set of mutually exclusive subunits, with each subunit having its own unique bundle of inputs and outputs. Examined as well in this area, is the presence of multiple processes for generating sets of outputs. Missing from that earlier work is consideration of the presence of outputs in the form of by-products, giving rise to a parent-offspring phenomenon. One of the modeling complications there is that the parent assumes two different roles; as an input affecting the offspring, while at the same time being the dominant output. This gives rise to a model that we refer to as conditional two-stage. Another complication is that in the presence of multiple processes, by-products often arise out of only a subset of those processes. In the current paper we develop a DEA-type of methodology to handle partial input to output impacts in the presence of by-products.     

基于DEA的两阶段系统中间产品公平设定研究

在由两个子决策单元串联组成的两阶段系统中,公平设定中间产品目标对确保和激励两阶段相互合作以达到整个系统的最佳性能至关重要.数据包络分析(DEA)作为系统绩效评估的一种非参数方法吸引了众多学者的注意;基于此方法,本文提出一个考虑公平关切的两阶段DEA模型用于设定两阶段系统中间产品目标,并证明据此模型获得的设定方案正是一个纳什讨价还价博弈的均衡解.