改进AHP主观性的DEA/AHP新模型

本文提出了一种DEA(数据包络分析)与AHP(层次分析法)结合的新模型。该模型有别于目前国内很多将AHP作为约束锥的DEA混合模型。通过对模型的阐述和算例的验证,得到了DEA/AHP方法相对于DEA的优势。并指出了以AHP作为约束锥的DEA混合模型的不足,以及本文提出的DEA/AHP方法对于AHP主观性的改进,并实现了DEA方法中决策单元的完全排序。     

基于DEA交叉模型的城市物流能力评价

从城市物流能力的内涵分析人手,结合城市物流能力的特点,构建了城市物流能力评价指标体系.在对比分析了多种评价方法的优缺点之后,针对数据包络分析(Data Envelopment Analysis,简称DEA)模型评价城市物流能力时存在多个有效决策单元的问题,引入交叉机制,得到明确的城市物流能力排序,在此基础上对柳州市各个县区的物流能力进行了综合评价,为柳州市城市物流规划提供理论支持.     

高校科研管理绩效评价中数据包络分析模型研究

目前在高校科研管理绩效评价研究中使用了数据包络分析,但现有模型与高校科研现状有一定差距,在学科差异、学校差异和指标选取方面存在一定不足。本研究提议引入加权DEA模型、交互基准群DEA模型、非期望产出DEA模型加以完善。     

区域技术创新体系评价模型研究

本文首先探讨了区域创新体系的结构特征，在此基础上从区域技术创新体系的基点、构架、动力三个方面建立了区域技术创新体系的评价指标体系，按照投入产出的关系将这些指标分为输入指标和输出指标，从而运用DEA的方法对大连、沈阳和哈尔滨三个城市进行了评价，得出了制度和法律建设在区域技术创新体系的建设中有着举足轻重的作用。     

基于DEA的物流公司绩效评价

鉴于传统绩效评价方法的局限性,运用数据包络分析（DEA）法,并以2008和2009年19家中国物流上市公司为样本进行实证研究,为各物流企业客观了解和评价自身物流竞争力和相对物流绩效水平提供参考。     

基于DEA模型的陕西省装备制造业竞争力分析

随着新一轮经济改革和发展周期的形成,作为陕西支柱产业的装备制造业,将面临更大的调整和机遇,研究和认识陕西装备制造业的发展水平将具有着重要的现实意义。本文采用数据包络分析模型对陕西省装备制造业中7大行业的产业竞争能力进行实证分析研究,探求陕西装备制造业的整体发展水平。     

基于DEA模型的钢铁企业规模经济有效性研究

数据包络分析方法（DEA）是分析企业相对有效性的一种行之有效的方法,鉴于钢铁经济是我国国民经济的重要组成部分,文中用数据包络分析方法分析我国钢铁企业的规模经济有效性,结果表明,我国大部分钢铁企业的规模经济是无效的,最后,结合研究结果给出一些提高钢铁企业规模经济的建议。     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

学生学习状况评价与预测研究

现行的评价方式单纯的根据"绝对分数"评价学生的学习状况,忽略了基础条件的差异;只对基础条件较好的学生起到促进作用,对基础条件相对薄弱的学生很难起到鼓励作用。因此,研究学生学习状况的评价问题具有重要的理论与现实意义。本文根据学生学习情况,对数据进行分析评价,并据此通过数学建模的方法,对学生成绩进行定量分析,以其全面、客观、合理的评价和预测学生的学习状况。     

The Effect of Sample Size on the Mean Efficiency in DEA: Comment

Zhang and Bartels (1998) show formallyhow DEA efficiency scores are affected by sample size. They demonstratethat comparing measures of structural inefficiency between samplesof different sizes leads to biased results. This note arguesthat this type of sample size bias has much wider implicationsthan suggested by their example. Models which implicitly restrictthe comparison set like some models for non-discretionary variableslead to biased efficiency scores as well. A reanalysis of theBanker and Morey (1986b) data shows that the efficiency scoresderived there are significantly influenced by the variation insample size implicit in their model.     

The Use of Data Envelopment Analysis with Probabilistic Assurance Regions for Measuring Hospital Efficiency

This paper uses Data Envelopment Analysis (DEA) for an estimation of the cost efficiency of 70 Danish hospitals. The analysis relates to a cost function based on 483 outputs in combination with a set of probabilistic assurance regions defined by the cost distributions for each output. It is demonstrated that the probabilistic assurance region approach allows for (i) a frontier estimation in the full output space, i.e., no fixed aggregation is required, and (ii) a controlling of the variation in heterogeneity of the output clusters, in casu Diagnosis Related Groups. The likelihood of the estimated efficiency score for a given hospital can be measured based on the sensitivity of the score w.r.t. the probability levels used in the specification of confidence intervals for the probabilistic assurance regions.     

Extending Productivity Research Frontiers: DEA Resource of Datasets and Errata

Website databases provide the opportunity to fundamentally change the way research and knowledge are advanced and disseminated. This paper describes a website, http://www.etm.pdx.edu/dea/dataset/, focused on Data Envelopment Analysis and DEA researchers.     

Benchmarking Warehousing and Distribution Operations: An Input-Output Approach

We developan input-output model of a warehouse system to assess operationalefficiency. Our model simultaneously accounts for all of thecritical resources (labor, space, storage and handling equipment)and the different workload requirements (broken case, full caseand pallet picking, storage and order accumulation) of a warehouse.We collected extensive data on 57 warehouse and distributionfacilities from a variety of industries, including auto parts,dental and office supplies, electronics, fine papers, hardware,health care, industrial packaging, mail order apparel, officemachines, photographic supplies, and wholesale drugs, and usedthe model to assess and compare their efficiencies. We offer3 conclusions based on a statistical analysis of the operatingefficiencies obtained from several models: Smaller warehouses tend to be more efficient than larger warehouses.Warehouses using lower levels of automation tend to be moreefficient. This association is more pronounced in small firms.Unionization is not negatively associated with efficiencyand in fact may actually contribute to higher efficiency.     

On the Role of Weight Restrictions in Data Envelopment Analysis

This paper examines the role that weight restrictions play in Data Envelopment Analysis (DEA). It is argued that the decision to include a factor (input or output) in a DEA model represents an implicit judgement that the factor has a non-trivial weight. It therefore seems perverse to allow DEA to assign a trivial weight to that factor in assessing the efficiency of a unit. There is therefore a strong case for imposing restrictions on factor weights. However, many existing methods of weight restriction are in practice unwieldy. This paper proposes an alternative approach we term contingent weight restriction which is both practical and intellectually consistent with the DEA philosophy. The paper explores the implications of alternative methods of weight restriction using simulated data from a well known production process.     

Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates

Many studies devoted to efficiency performance evaluation in the education sector are based on measures of central tendency at school level as, for example, the average values of students belonging to the same school. Although this is a common and accepted way of summarizing data from the original observations (students), it is not less true that this approach neglects the existing dispersion of data, which may become a serious problem if variability across schools is high. Additionally, imprecision may arise when experts on each evaluated subject select the battery of questions, with different levels of difficulty, which will be the base for the final questionnaires completed by students. This paper uses data from US students and schools participating in PISA (Programme for International Student Assessment) 2015 to illustrate that schools' efficiency measures based on aggregate data and imprecision may reflect an inaccurate picture of their performance if they are compared to measures estimated accounting for broader information provided by all students of the same school. In order to operationalize our approach, we resort to Fuzzy Data Envelopment Analysis. This methodology allows us to deal with the notion of fuzziness in some variables such as the socio-economic status of students or test scores. Our results indicate that the estimated measures of performance obtained with the fuzzy DEA approach are highly correlated with those calculated with traditional DEA models. However, we find some relevant divergences in the identification of efficient units when we account for data dispersion and vagueness.     

Sensitivity and stability of efficiency classifications in Data Envelopment Analysis

A new technique for assessing the sensitivity and stability of efficiency classifications in Data Envelopment Analysis (DEA) is presented. Here developed for the ratio (CCR) model, this technique extends easily to other DEA variants. An organization's input-outut vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. For the l ₁, l and generalized l norms, the radius of the maximal cell can be computed using linear programming formulations. This radius can be interpreted as a measure of the classification's stability, especially with respect to errors in the data.Abraham Charnes passed away December 19, 1992.     

Combining PCA With DEA to Improve the Evaluation of Project Performance Data: A Taiwanese Bureau of Energy Case Study

This article proposes a new method of project performance evaluation, by which project performance data can be better understood. This article combines principal component analysis (PCA) and data envelopment analysis (DEA) to enhance the efficiency of decision‐making units more accurately. The data used was based on energy projects promoted by the Bureau of Energy at the Ministry of Economic Affairs in Taiwan. The results of this article show that by combining PCA and DEA in evaluating the performance of energy projects, there was an improved evaluation of projects over simply using DEA data alone.     

A note on “A directional slacks-based measure of technical inefficiency”

In this note we show, by means of several examples, that a directional distance function model cannot be matched with an additive model. Specifically, we show the difference between the directional slacks-based measure and the corresponding directional distance function.     

Adjusting for the environment in DEA: A comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.     

DEA在仓库运作管理中的应用

数据包络分析能对决策单元的有效性进行评价。本文应用数据包络分析的C2R模型,对仓库的运作效率进行研究。选取某部的8个备件仓库为样本,基于C2R模型计算结果,得到一些有价值的仓库运作管理方面的信息。