Review of Methods for Increasing Discrimination in Data Envelopment Analysis

We present a review of methods for increasing discrimination between efficient DMUs in Data Envelopment Analysis. These methods were classified into two groups: those that incorporate a priori information and those that do not use or minimize the use of such a priori information. We also compare these methodologies regarding their specific characteristics.     

Preference, Production and Performance in Data Envelopment Analysis

This paper attempts to provide a systematic approach to the DEA model building. To this end, we try to identify some essential aspects of DEA modelling. Three key building blocks in a DEA model are identified: they are preference order, production possibility set and performance measure. It is shown that the preferences and performance measurements used in the standard DEA models are only particular examples in this framework. It is also illustrated in this work that this methodology is useful in building new DEA models to handle nonstandard applications such as those involve non-Pareto preferences or undesirable inputs-outputs.     

Relative Efficiency Assessments Using Data Envelopment Analysis: An Application to Data on Rates Departments

This paper examines the nature of information obtained from data envelopment analysis (DEA) in comparative studies of the efficiency of decision-making units, and it discusses the interpretation and practical usefulness of such information. The themes developed in the paper are illustrated by an application of DEA to data on the rate-collection function of London Boroughs and Metropolitan District Councils. The paper begins with an overview of DEA, followed by a discussion of some of the practical considerations arising in the application of DEA. It then describes the structuring of the rate-collection function for assessment by DEA, and explores the extent to which units can be classified as relatively efficient or inefficient. In respect of relatively inefficient units, it illustrates the construction of target inputs and outputs so that their relative efficiency may improve. In respect of relatively efficient units, it is argued that their identification is weak in the sense that for some of them their apparent efficiency may be simply a reflection of an uncommon input-output profile. It is shown, nevertheless, that information about relatively efficient units can be used to identify those of them which may prove examples of good operating practice in given aspects of their function. (Readers not familiar with British taxes may wish to note that rates are a tax on property, levied by local authorities.)     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.     

Fuzzy BCC Model for Data Envelopment Analysis

Fuzzy Data Envelopment Analysis (FDEA) is a tool for comparing the performance of a set of activities or organizations under uncertainty environment. Imprecise data in FDEA models is represented by fuzzy sets and FDEA models take the form of fuzzy linear programming models. Previous research focused on solving the FDEA model of the CCR (named after Charnes, Cooper, and Rhodes) type (FCCR). In this paper, the FDEA model of the BCC (named after Banker, Charnes, and Cooper) type (FBCC) is studied. Possibility and Credibility approaches are provided and compared with an -level based approach for solving the FDEA models. Using the possibility approach, the relationship between the primal and dual models of FBCC models is revealed and fuzzy efficiency can be constructed. Using the credibility approach, an efficiency value for each DMU (Decision Making Unit) is obtained as a representative of its possible range. A numerical example is given to illustrate the proposed approaches and results are compared with those obtained with the -level based approach.     

A Semi-Infinite Programming Model In Data Envelopment Analysis

We consider a model for data envelopment analysis with infinitely many decision-making units. The determination of the relative efficiency of a given decision-making unit amounts to the solution of a semi-infinite optimization problem. We show that a decision-making unit of maximal relative efficiency exists and that it is 100% efficient. Moreover, this decision-making unit can be found by calculating a zero of the semi-infinite constraint function. For the latter task we propose a bi-level algorithm. We apply this algorithm to a problem from chemical engineering and present numerical results     

A Special Algorithm for an Additive Model in Data Envelopment Analysis

A special algorithm is presented for the additive model in data envelopment analysis (DEA). The special algorithm first classifies a data set into several subsets. Then the subset is solved by a different algorithmic framework. In simulation studies, the algorithm outperformed available DEA codes. The proposed algorithm can efficiently deal with a large data set.     

The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation

While financial ratios are currently the method most often used to evaluate a bank's performance, there is no clear-cut rationale which would allow one to acquire a composite score on the overall financial soundness of a bank. This paper demonstrates the application of DEA (Data Envelopment Analysis) in conjunction with financial ratios to help bank regulators in Taiwan not only to distinguish the efficient banks from the inefficient ones but also to gain insight into various financial dimensions that somehow link to the bank's financial operational decisions.     

模糊数据包络分析方法有效性分析

传统数据包络分析(DEA)模型只能用来评价具有精确投入和产出数据的决策单元.然而在实践中决策单元的投入产出数据可能存在一定模糊性.为了评价具有模糊投入产出数据的决策问题,研究工作者提出了模糊数据包络分析模型,并给出了相应的有效性定义.对于不同研究者提出的有效性定义方式有众多地方需要改进.通过这些改进提出了相关模型及新的有效性定义方式,并给出了相关实例.     

DEA方法进行规模收益分析的几点注记

自从 Banker等人 ( 1 984)用 DEA方法进行规模收益分析 ,已有越来越多的学者也在进行相关的研究 .研究主要从两个方面进行 :基于 DEA输入模型的方法和基于 DEA输出模型的方法 .这两类方法所应用的模型、条件、结论都有所不同 ,但在应用中有些文章将它们混淆在一起 .本文针对应用中容易出现的错误 ,给出反例和正确的判别规模收益状况的充分必要条件 .并由规模收益的定义 ,建议使用 DEA—输出模型进行规模收益分析 .     

组合DEA方法与成熟度模型对项目效益的评价

为全面考虑资金、管理决策能力等因素对项目效益的影响，本运用数据包络分析与项目成熟度模型结合的方法来对企业各个项目之间的相对效益进行评价，应用结果表明该评价方法对于企业资源的最优配置、提高总体效益是十分有用的。     

Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis

We contrast the different approaches of Data Envelopment Analysis (DEA) and Multiple Criteria Decision Making (MCDM) to superficially similar problems. The concepts of efficiency and Pareto optimality in DEA and MCDM are compared, and a link is demonstrated between the ratio efficiency definition in DEA and a distance measure in input–output space based on linear value functions. The problem of weight sensitivity is discussed in terms of value measurement theory, highlighting the assumptions needed during model formulation in order to justify the use of value judgements to constrain weight flexibility in DEA. Finally, we propose a stochastic approach, in which a probability distribution on efficiencies can be derived for each decision making unit, as a basis for comparison.     

The Potential for Endogeneity Bias in Data Envelopment Analysis

Data envelopment analysis has become an important technique for modelling the relationship between inputs and outputs in the production process, particularly in the public sector. However, whenever measures of the output of public sector activity receive public attention, there is a strong possibility that there will be a feedback from the achieved output to the resources devoted to the activity. In other words, the level of resources is endogenous. The implications of such endogeneity for standard econometric estimation techniques are well known, and methods exist to deal with the problem. Most commentators have assumed that endogeneity poses no analogous problems for DEA because the technique merely places an envelope around feasible production possibilities. Using Monte Carlo simulation techniques, however, this paper shows that the efficiency estimates generated by DEA in the presence of endogeneity can be subject to bias, in the sense that inefficient units using low levels of the endogenous resource may be set tougher efficiency targets than equally inefficient units using more of the resource, particularly when sample sizes are small. The paper concludes that, in such circumstances, great caution should be exercised when comparing efficiency measures for units using different levels of the endogenous input.     

数据包络分析在高等教育绩效测评中的应用研究综述

数据包络分析自从20世纪70年代末被著名运筹学家Charnes等人提出以来,与层次分析法一样被广泛应用到各种生产领域的评价问题中,尤其是对多输入和多输出系统的绩效评价问题.而高等教育就是一个典型的多输入和多输出系统.通过总结数据包络分析法在大学整体办学效益评估、大学内部院系绩效评价、大学科学研究绩效评价、大学人才培养绩效评价等方面的应用成果,指出了应用数据包络分析研究高等教育绩效的不足并展望了未来可能的研究重点.     

Data Envelopment Analysis of multiple response experiments

Taguchi method is the usual strategy in robust design and involves conducting experiments using orthogonal arrays and estimating the combination of factor levels that optimizes a given performance measure, typically a signal-to-noise ratio. The problem is more complex in the case of multiple responses since the combinations of factor levels that optimize the different responses usually differ. In this paper, an Artificial Neural Network, trained with the experiments results, is used to estimate the responses for all factor level combinations. After that, Data Envelopment Analysis (DEA) is used first to select the efficient (i.e. non-dominated) factor level combinations and then for choosing among them the one which leads to a most robust quality loss penalization. Mean Square Deviations of the quality characteristics are used as DEA inputs. Among the advantages of the proposed approach over traditional Taguchi method are the non-parametric, non-linear way of estimating quality loss measures for unobserved factor combinations and the non-parametric character of the performance evaluation of all the factor combinations. The proposed approach is applied to a number of case studies from the literature and compared with existing approaches.     

Comparison of Methods for Image Analysis on cDNA Microarray Data

Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses such as clustering or the identification of differentially expressed genes. This article reviews a number of existing image analysis approaches for cDNA microarray experiments and proposes new addressing, segmentation, and background correction methods for extracting information from microarray scanned images. The segmentation component uses a seeded region growing algorithm which makes provision for spots of different shapes and sizes. The background estimation approach is based on an image analysis technique known as morphological opening. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing. The statistical properties of the different segmentation and background adjustment methods are examined using microarray data from a study of lipid metabolism in mice. It is shown that in some cases background adjustment can substantially reduce the precision—that is, increase the variability—of low-intensity spot values. In contrast, the choice of segmentation procedure has a smaller impact. The comparison further suggests that seeded region growing segmentation with morphological background correction provides precise and accurate estimates of foreground and background intensities.     

Technological leadership and variety: A Data Envelopment Analysis for the French machinery industry

We investigate structure and structural change within the French machinery industry from 1984–1991 in order to detect the apparent technology leaders and to get an account of the technological variety within the sector. The theoretical background of the paper is found in modern approaches to the economics of innovation and technology, where the very nature of technological knowledge and the local character of technological change are seen as a fundamental reason for the use of different technologies and for the different performances of firms. We apply a procedure that allows us to take into account such different performances and variety, Data Envelopment Analysis (DEA). As one major result, we find several best-practice technologies as well as a measure of technical inefficiency, allowing us to classify firms with respect to their relative technical performance. Moreover, technology leaders can be assigned to specific technology fields. The change within and between these fields over time is investigated.     

一种新的带有参数的DEA有效单元排序模型

本文对数据包络分析中的有效单元排序方法进行了研究, 从一个新 的角度,定义最优有效和最劣无效, 提出了一种带有参数的有效单元排序模型.本文还给出并证明了此模型的一些性质, 并与其他排序模型进行了比较, 证明了本文模型的优越性. 最后用一个实例, 检验了此模型的可行性.