基于DEA模型的体系作战目标节点价值评价

根据基于信息系统的体系作战的特点,构建了目标节点价值评价指标体系,将评价指标分为成本型和效益型两部分,分别作为DEA评价模型决策单元的输入指标和输出指标,并对多个DEA有效的决策单元运用超效率DEA模型进行了分析和评价,取得了较好的评价效果.     

应用Matlab解决常用DEA模型的评价分析

阐述多种常用的DEA评价模型,利用矩阵形式表述模型结构,应用Matlab对这些模型的评价程序进行编写.使常用的DEA评价问题可以通过Matlab软件解决,有效地解决了DEA循环计算导致的计算量大和复杂的问题.编写的程序分别为传统DEA-CCR模型及其对偶模型、BCC模型、最劣CCR模型和超效率DEA,为以后的DEA评价问题和模型改进提供了便利.     

基于基尼系数-交叉效率的多属性决策方法

在数据包络分析(DEA)方法的基础上,提出一种基于基尼系数-交叉效率的多属性决策方法,用于解决具有多个投入、产出指标的多属性决策问题.首先,借鉴基尼系数的优化准则构建基尼系数-交叉评价策略模型,从而得到相对唯一的DEA交叉效率矩阵;然后,应用基尼准则计算各个效率值所包含的信息纯度,并借之实现交叉效率矩阵的集结;最后,根据集结结果对决策单元进行排序和择优.所提决策方法不仅能够克服传统DEA交叉效率方法的交叉评价策略选择难的问题,而且能够保证决策过程的客观性和公平性.同时,所提方法还对交叉评价所得的决策信息进行提纯,为科学合理地进行决策提供更多的有效信息.通过对中国各地区的医疗资源配置效率进行实证,验证了所提出方法的有效性和实用性.     

基于DEA的高校院系运行效率评价

数据包络分析在关于多指标输入和输出的效率评价中具有独特的优势。本文以天津某高校为例,选取了三个投入指标和四个产出指标,借助DEAP软件分别对该高校的十个院系运行效率进行了DEA评价,最终找出了"DEA相对有效"和"非有效"的院系,并针对"非有效"院系提出了相关的改进建议。     

基于DEA的装备费效分析

应用数据包络分析(DEA)方法对装备费效进行了综合评价。在评价结果的基础上,通过DEA二次相对评价法对所有决策单元进行了排序,并根据输出DEA模型分析了各决策单元的规模收益状况。最后利用决策单元在相对有效前沿面上的投影分析,明确了非有效单元投入指标的调整量,为优化装备费用规模提供决策支持。     

考虑环境处置性的DEA综合效率模型

由于传统的径向或非径向DEA（Data Envelopment Analysis）模型在追求投入或产出最大化改进时角度不同,并且模型中的污染物往往采用强可处置假设,考虑因此造成的评价偏离实际生产过程、区分力差等问题,提出环境弱处置性下的基于灰关联的DEA径向与非径向综合效率模型。基于环境DEA技术提出了一个径向与非径向特征结合的综合效率模型研究框架;基于投入变量序列的灰关联构造了接近度指标,从而获得框架中连接与测量径向与非径向模型的特征参数,将其置于框架中得到最终评价结果。以2010年中国29个省份综合效率评价为算例,结果验证了模型的有效性。     

靶场试验任务绩效评估方法研究

针对靶场试验任务存在的评估标准特殊、评估指标特殊、评估主体特殊等特殊性,对靶场试验任务的绩效评估方法进行了研究.靶场试验任务评估的难点在于结合其特殊性设计科学合理的评估指标,绩效评估结果应当客观,还能够指导后续靶场试验任务的开展.结合DEA模型的适用性特点,参照相关质量文件和评估主体所关心的内容,设计输入和输出指标,对靶场试验任务绩效评估进行研究.通过分析多次靶场试验任务的技术效率、纯技术效率和规模效率,找出了非最优靶场试验任务效率不足的问题和原因,得到了靶场试验任务的绩效评估排序以及其绩效改进点.结果表明,将DEA模型应用到靶场试验任务的绩效评估中,评估结论较为客观,绩效改进分析能够很好地指导后续靶场试验任务的开展.     

考虑环境因素的企业DEA 有效性分析

对企业投入-产出效率进行评价应将环境指标与经济指标相结合。利用数据包络分析（DEA）技术，建立了考虑环境因素的企业效率评价模型。在生产可能集不满足锥性的假设下，证明了新模型与原模型具有相同的有效性。并用一个算例进行说明。     

基于DEA交叉评价的模糊综合评价模型及其应用

借鉴数据包络分析(DEA)交叉评价的思想,首先将评价系统内的指标分为量化指标和非量化指标,在定义平均交叉效率、最小交叉效率和最大交叉效率概念的基础上,采用交叉评价方法对量化数据进行处理;然后对最小交叉效率值、平均交叉效率值和最大交叉效率值进行模糊化,模糊化之后将其作为模糊综合评价的指标与非量化指标一起进行二次评价,以建立基于DEA交叉评价的模糊综合评价模型;最后通过评价实例验证了所提出的模型在处理客观数据与主观因素并存的多属性决策中的客观性和全面性.     

高校教学质量评价体系的思考与实践

文章分析了当前高等院校质量评价存在的问题,根据高等院校教育质量监控与保障的实际需求,结合作者在高校质量管理部门工作的经验,对高校质量评价体系提出建议：应建立责任明确的评价体系,实现多元主体广泛参与评价;有效安排评价内容,合理分配评价权重;选用适宜的检查方式,加强对检查结果的反馈与问题的督办。     

Scale and cost efficiency analysis of networks of processes

In this paper a simple way of computing technical, scale, cost and allocative efficiency scores for homogeneous networks of processes is presented. The system Production Possibility Set (PPS) is formed through the composition of the PPS of the individual processes, which, in turn, are modelled in the conventional, axiomatic way using observed data. Firstly, the overall system scale and technical efficiency are computed using the relational network DEA approach. Local Returns To Scale (RTS) can also be estimated with these models. Secondly, assuming the prices of exogenous inputs are known, a minimum cost network DEA model is solved, from which cost and allocative efficiencies are derived. The proposed approach is illustrated with a two-stage problem from the literature, showing the usefulness of a more detailed problem assessment both in terms of technical and scale efficiency and RTS and in terms of cost and allocative efficiency.     

Radial data envelopment analysis models with mixed orientation of input and output

This paper discusses parametric solutions and envelopment formulations of radial data envelopment analysis (DEA) models with mixed orientation of input and output. These solutions geometrically but not numerically lie between the two usual solutions from input and output orientations. The consequent results provide alternative optimal solutions between those from input‐ and output‐oriented CCR models for constant returns to scale DEA models and optimal scale efficiency in addition to technical efficient solutions from input‐ and output‐oriented BCC models for variable returns to scale DEA models.     

Analysis of overall technical efficiency,pure technical efficiency and scale efficiency in the medium-sized audit firms

This study uses the data envelopment analysis (DEA) to evaluate the operational efficiency of 173 medium-sized audit firms in 2005. The empirical result indicates that there are 24 audit firms with the overall technical efficiency value of 1. In terms of overall technical efficiency, pure technical efficiency and scale efficiency, the result shows that the average scale efficiency of all samples is higher than the average pure technical efficiency. Besides, 55 firms of 173 audit firms are in the stage of constant returns to scale, 18 firms are in the stage of increasing returns to scale, 100 of them are in the stage of decreasing returns to scale. Thus, most of medium-sized audit firms are in the stage of decreasing returns to scale. In addition, this paper finds that the larger the scale, the higher the above three efficiency values. The audit firms with higher business revenues have better operational efficiency. The overall technical efficiency and sale efficiency of the audit firms with branches are significantly higher than those without branches. The audit firms with larger number of employees and partners, they perform significantly better overall technical efficiency and sale efficiency. Finally, the audit firms with higher total expenditures also have significantly higher operational efficiency.     

Concurrent estimation of efficiency,effectiveness and returns to scale

In recent years, data envelopment analysis (DEA) has been widely used to assess both efficiency and effectiveness. Accurate measurement of overall performance is a product of concurrent consideration of these measures. There are a couple of well-known methods to assess both efficiency and effectiveness. However, some issues can be found in previous methods. The issues include non-linearity problem, paradoxical improvement solutions, efficiency and effectiveness evaluation in two independent environments: dividing an operating unit into two autonomous departments for performance evaluation and problems associated with determining economies of scale. To overcome these issues, this paper aims to develop a series of linear DEA methods to estimate efficiency, effectiveness, and returns to scale of decision-making units (DMUs), simultaneously. This paper considers the departments of a DMU as a united entity to recommend consistent improvements. We first present a model under constant returns to scale (CRS) assumption, and examine its relationship with one of existing network DEA model. We then extend model under variable returns to scale (VRS) condition, and again its relationship with one of existing network DEA models is discussed. Next, we introduce a new integrated two-stage additive model. Finally, an in-depth analysis of returns to scale is provided. A case study demonstrates applicability of the proposed models.     

An AHP/DEA method for measurement of the efficiency of R&D management activities in universities

In order to develop a better tool for the assessment of the management performance of research and development (R&D) activities in research‐oriented universities, a combination of analysis hierarchical process (AHP) and data envelopment analysis (DEA) is proposed for the assessment of the efficiency of R&D management activities in universities. The measure consists of the measurement of a university's previous and present R&D strength by AHP and the assessment of the relative efficiency of its growth in R&D strength against those of other universities by DEA, in which the management basis of the measured universities is taken into consideration. The application of the measure to assess the R&D management efficiency of 29 universities in China indicates the universities which have improved their management work achieved a high efficiency value regardless of whether their original R&D strengths were strong or weak. Such a measure is proved to be helpful for motivating the universities to keep on improving their R&D management.     

Measurement efficiency and productivity in SAS/OR

This paper explores the use of the optimisation procedures in SAS/OR software with application to the measurement of efficiency and productivity of decision-making units (DMUs) using data envelopment analysis (DEA) techniques. DEA was originally introduced by Charnes et al. [J. Oper. Res. 2 (1978) 429] is a linear programming method for assessing the efficiency and productivity of DMUs. Over the last two decades, DEA has gained considerable attention as a managerial tool for measuring performance of organisations and it has widely been used for assessing the efficiency of public and private sectors such as banks, airlines, hospitals, universities and manufactures. As a result, new applications with more variables and more complicated models are being introduced.Further to successive development of DEA a non-parametric productivity measure, Malmquist index, has been introduced by Fare et al. [J. Prod. Anal. 3 (1992) 85]. Employing Malmquist index, productivity growth can be decomposed into technical change and efficiency change.On the other hand, the SAS is a powerful software and it is capable of running various optimisation problems such as linear programming with all types of constraints. To facilitate the use of DEA and Malmquist index by SAS users, a SAS/MALM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear-programming models based on the selected DEA. An example is given to illustrate how one could use the code to measure the efficiency and productivity of organisations.     

Application of DEA in analyzing a bank’s operating performance

This article takes 117 branches of a certain bank in Taiwan in 2006 as the research subject and introduces data envelopment analysis (DEA) to evaluate the operating performances of business units of this bank to provide the reference for a bank’s managers in determining operation strategies. The result indicates that, in overall technical efficiency, the case bank has many inefficient branches distinctly; the average overall technical efficiency of branches is 54.8% and the average pure technical efficiency of branches is 67%, which is probably because of lower loan-to-deposit ratio, leading to excessive input waste. The average scale efficiency of the case bank during the sample period is 82%. The ratio of resource waste due to technical inefficiency is 45.2%, of which 55.03% is due to pure technical inefficiency.     

Technology forecasting using DEA in the presence of infeasibility

As a predictive application of data envelopment analysis (DEA), technology forecasting using DEA (TFDEA) measures the rate of frontier shift by which the arrival of future technologies can be estimated. However, it is well known that DEA and therefore TFDEA may suffer from the issue of infeasible super‐efficiency especially under the condition of variable returns to scale. This study develops an extended TFDEA model based on the modified super‐efficiency model proposed in the literature, which has the benefit of yielding radial super‐efficiency scores equivalent to those obtained from the original super‐efficiency model when feasibility is present. The previously published application of liquid crystal displays (LCD) is revisited to illustrate the use of the new model. The results show that the proposed approach makes a reasonable forecast for formerly infeasible targets as well as a consistent forecast for feasible targets.     

Evaluating alternative DEA models used to control for non-discretionary inputs

Evaluation of performance using DEA requires models consistent with the underlying technology. There have been a number of models proposed for analyzing performance in the presence of non-discretionary inputs. Banker and Morey (Operations Research 34 (1986) 513–521) provided the first DEA model to measure technical efficiency. Other single- and multiple-stage models that incorporate DEA have been developed. This paper discusses the various approaches and provides a simulation analysis to compare the relative performance of each.     

Measuring the efficiencies of Asia–Pacific international airports – Parametric and non-parametric evidence

The purpose of this paper is to estimate the efficiencies of, and to discuss the managerial implications for 12 international airports in the Asia–Pacific region based on data from the period 1998–2006. We applied data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to compute efficiency estimates, and the empirical results are discussed in terms of management perspectives and mathematical analysis. From the management perspectives, we suggest that airports should focus more on investment than on human resources. In addition, we found that inefficiency effects associated with the production functions of airports increased over the investigated period. From the perspective of mathematical analysis, we determined that deviations from the efficient frontiers of production functions are largely attributed to technical inefficiency. Finally, the empirical results imply that employing the discretion to adjust the scale size of the production function appears to improve efficiency. The main contribution of the paper is in showing how DEA and SFA can be used together to complement each other.