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     

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。     

两阶段生产系统的DEA效率评价模型

数据包络分析(DEA)作为一种数学规划方法,已经被广泛用来评价一个决策单元相对于其它决策单元的效率。经典的DEA模型把决策单元看作一个“黑箱”,对决策单元的内部运行机制不作深入的研究。本文以一个两阶段生产系统为例,从生产系统的内部过程出发,提出一个基于DEA的模型以合理评估该决策单元的相对效率。本文提出的模型实质上是一类特殊的网络DEA模型,其评价原理有别于已有的研究成果,但确实有助于管理者确定生产过程(如供应链)的非有效来源及其效率改进方向。     

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。     

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。     

区间DEA模型求解算法及其在项目投资效率评价中的应用

当决策单元的变量取值区间范围较大时,经典区间DEA求解算法求得的相对效率区间长度也可能较大,对决策单元有效性的解释力低,很难直观反映相对效率的大小。将决策单元的变量区间划分为若干个子区间,分别计算决策单元在各子区间上的DEA效率,进而求得综合效率区间,作为评价决策单元有效性的基准。综合效率区间的区间长度比经典算法的求解结果小,将新算法应用于投资项目的效率评价,便于对投资项目的效率大小进行比较,进而为项目投资决策提供科学依据。     

基于DEA模型的政府绩效相对有效性评估

政府绩效评估是指对政府公共行为的投入、产出和实际成果的测量与评估。政府绩效评估过程涉及到对政府绩效投入和产出的多个数量指标进行测量、分析和评估。DEA综合模型是一个基于相对效率概念而发展起来的一种新的绩效评估方法。它对评估具有多投入和多产出的政府绩效，特别是对比不同政府部门之间绩效的相对关系有着天然的优势。     

基于整体效率的区间DEA方法研究

针对现有的区间DEA方法采用非统一指标数据进行效率评价的不足,本文在分析现有区间DEA方法的基础上,提出一种基于整体效率的区间DEA方法。该方法能够在多决策单元系统整体效率最大化的同时,得到统一的各决策单元投入/产出的精确数据及各指标权重,一次性求解出所有决策单元的效率。最后,采用一个算例分析说明文章所提方法的合理性和优越性。     

基于可变权重的DEA效率评价模型

传统DEA模型将投入、产出权重视为固定变量。为了更合理地对决策单元的效率进行评价,本文研究了如何将有关投入、产出变量的权重的更多信息融入效率评价模型,提出了可变权重的概念,给出了一种基于可变权重的DEA效率评价模型。本文模型是CCR模型的推广。在有关权重信息可得的前提下,本文模型较之CCR模型中效率评价上更为合理。但是,如何准确度量投入、产出权重并将其函数化,是本文方法应用的难点。一个算例分析演示了本文模型。     

Konsensuale Effizenzbewertung und -verbesserung mittels DEA

This contribution is a new DEA approach in as much as it permits the decision making units (DMUs) to improve their respective efficiencies from the view of a peer rather than form their own self-appraisal attitudes. These authors study the output oriented model under constant scale efficiencies and first develop how a DMU can improve its performance by radial output increase or even by free output variation??in the light of the weight system of an arbitrary peer. The results are an improved cross-efficiency matrix and a maximum cross-efficiency matrix. Either of these matrices may serve as an appropriate instrument for a consensual choice of a peer??consensual among all DMUs. Input oriented models as well as simultaneous input/output considerations amend the so far developed results. A suitable example demonstrates all aspects of the new approach.     

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.     

Eco-efficiency analysis of paper mills along the Huai River: An extended DEA approach

The objective of this paper is to estimate the ecological efficiency of paper mills along the Huai River in China. The main characteristic of the ecological efficiency evaluation problem is that an undesirable output of biochemical oxygen demand (BOD) and a non-discretionary input (BOD emission quota) should be considered simultaneously. By analyzing the impacts of the non-discretionary input on decision-making units’ (DMUs) desirable and undesirable outputs, a non-radial output-oriented DEA model is proposed. In the proposed model, we describe a new approach of defining reference set that requires reference units operate in a similar environment on average. We employ the model to provide efficient inputs/outputs targets for DMU managers to improve DMUs’ efficiencies. Based on the developed model, impacts of the non-discretionary input on DMUs’ returns are also analyzed. We illustrate the proposed model, using real data, for 32 paper mills along the Huai River in China.     

基于最近距离投影的DEA两阶段效率评价方法研究

数据包络分析是进行效率评价最重要的方法之一。传统的数据包络分析理论主要寻找有效前沿面上的最远距离投影，在极大化无效性指数的同时也面临着效率改进的巨大难度和高额成本。对于具有两阶段内部生产结构的决策单元，本文从考虑最小改进难度的视角出发，提出了最近距离投影的两阶段效率评价方法。该方法首先得到所有强有效决策单元的线性组合，且这些组合均占优于被评价的两阶段决策单元。然后建立了两阶段范围调整效率评价模型，在确定具有最近投影距离的占优组合的同时，得到了两阶段评价效率。最后，本文运用我国32家上市银行的年度数据对所提出方法进行了应用验证。     

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.     

An integrated model for slack-based measure of super-efficiency in additive DEA

As a non-radial approach, a super-efficiency model, Super SBM, was proposed by Tone [15] to rank efficient DMUs. Du et al. [7] extends the Super SBM model to the additive (slacks-based) DEA model. To obtain the super-efficiencies of the DMUs, one needs to identify the efficient DMUs first and then apply the additive super-efficiency model to those efficient DMUs. In this paper, we propose an integrated model so that the efficiencies of the inefficient DMUs and the super-efficiencies of the efficient DMUs can be obtained by a single model. The efficiency scores obtained by our integrated model are the same as those obtained by Du et al. [7] and the additive DEA model.     

A slack-based measure of efficiency in context-dependent data envelopment analysis

Data envelopment analysis (DEA) has been proven as an excellent data-oriented performance evaluation method when multiple inputs and outputs are present in a set of peer decision-making units (DMUs). In the DEA literature, a context-dependent DEA is developed to provide finer evaluation results by examining the efficiency of DMUs in specific performance levels based upon radial DEA efficiency scores. In DEA, non-zero input and output slacks are very likely to present after the radial efficiency score improvement. Often, these non-zero slack values represent a substantial amount of inefficiency. Therefore, in order to fully measure the inefficiency in DMU's performance, it is very important to also consider the inefficiency represented by the non-zero slacks in the context-dependent DEA. This study proposes a slack-based context-dependent DEA which allows a full evaluation of inefficiency in a DMUs performance. By using slack-based efficiency measure, we obtain different frontier levels and more appropriate performance benchmarks for inefficient DMUs.