基于L-R型模糊DEA的模型改进

本文首先介绍了前人建立的模糊DEA模型,考虑这些模型存在人为提高效率值、未充分利用模糊信息、计算量过大的问题,建立了一个新的L-R DEA模型,该模型基于α-截集的模糊数变换,将指标集分解为多个指标子集,分别对每个子集进行效率计算,最后应用熵值法确定决策单元(DMU)的最终效率值。该方法可以对DMU进行充分排序,扩大了DEA的应用领域。文末通过一算例来说明新模型的有效性。     

基于不同DEA模型的组合评价方法研究

为了解决传统CCR模型不能对决策单元进行充分排序的难题,介绍了四种DEA模型,包括自评型、竞争型、合作型、竞合型DEA。分析每种模型的功能与现实意义,由于单独使用每种模型评价决策单元效率都存在一定的片面性,因此本文采用熵值法来确定四种方法的权重,综合考虑不同DEA模型的评价结论,从而能够对决策单元进行充分排序。文末通过一个算例来证明方法的有效性。     

基于改进的DEA模型在效率评价中的应用

DEA模型是目前最常用的效率评价方法,但是传统DEA模型输入和输出指标权重分配不合理,导致无法对有效的决策单元加以区分。在DEA模型中引入灰色关联约束锥,对权重进行约束。通过将该模型应用于高校评价,验证了该模型可以在信息不充分时解决指标权重分配和决策单元排序问题,使评价结果更为合理。     

各行业企业物流发展不平衡性评价分析

重新整合数据包络分析法(DEA),在传统CCR模型基础上,补充决策单元(DMU)排序方法.使其不但能够指出各决策单元是否有效及无效的原因和程度.同时得到各决策单元的优先排序。并利用该方法对2003-2004年我国十个行业的企业物流发展状况进行评价分析.指出各行业企业物流发展的特点及其存在问题.并对这十个行业的企业物流发展水平进行了排名。     

基于残缺值的模糊偏好关系多属性大群体决策方法

在多属性大群体决策环境下，针对决策者给出关于决策方案两两比较且可能包含残缺值的模糊偏好关系的决策问题，提出了一种基于相似度聚类的残缺值模糊偏好关系大群体决策方法。此方法首先通过标准化残缺值矩阵，然后定义判断矩阵之间的相似度对大群体进行聚类，再对各个属性下的群体偏好进行集结，通过偏差熵模型确定各个决策属性的权重，集结所有决策属性下的群体偏好，最后得到决策方案的排序结果。文章最后给出了一个算例分析以验证此方法的有效性。     

基于borda序值法和模糊层次分析法对跨国公司R&D外包的风险度量

本文采用德尔菲法识别研发外包项目中可能出现的主要风险,再运用改进后的风险矩阵对已识别出的主要风险因素量化分析,然后采用Borda序值法初步对风险因素初步排序,最后运用模糊层次分析法对各风险要素进行权重计算并排序,确定风险的相对大小。本文采用的模糊层次分析法主要包括建立层次结构模型、构造模糊互补判断矩阵、计算各风险因素的权重、模糊互补判断矩阵的一致性检验以及总排序。     

基于超效率模型的石油天然气上市公司投资价值评价

DEA方法的CCR模型难以解决相对有效单元进一步识别的问题,而超效率模型通过重新定义生产可能集,可以对决策单元进行充分排序和评价。本文以中国9家石油天然气上市公司为样本,运用CCR模型和超效率模型,对2011年下半年石油天然气上市公司的经营效率进行了实证分析,结果表明,超效率值大小的顺序即是各个公司经营效率的强弱排序,对投资者投资具有较高的参考价值。     

基于超效率DEA和Tobit模型的中国上市煤炭企业经营效率现状分析

本文运用数据包络分析（DEA）方法和Tobit回归模型,首先选取2011年沪深两市具有代表性的18家煤炭上市公司为研究对象,以货币资金、流动资产合计、长期股权投资、固定资产和非流动资产合计为投入变量,主营业务收入、主营业务利润、利润总额为产出变量,运用CCR模型从技术效率、纯技术效率和规模效率等方面对其经营效率进行评价和分析;其次运用超效率DEA模型对CCR模型下DEA有效的决策单元计算其超效率值,从而为所有决策单元提供完整的效率值排序;最后,本文运用Tobit模型,分析影响中国上市煤炭企业经营效率的重要因素,为决策者提供提高企业经营效率的方向和政策。     

基于GDP水平的区域技术创新效率测度

区域技术创新效率是区域发展的动力,是衡量区域技术创新运营机制有效程度的重要标准,反应一个地区的研发水平和投入资源的利用率。通过建立区域技术创新效率指标体系,根据2009年我国30个省、市、自治区人均GDP构造系数矩阵,确定区域间的竞合关系;应用带有系数矩阵的交叉效率模型对2009年我国30个省、市、自治区的技术创新效率进行测度得出交叉效率矩阵,最后应用熵值法确定权重并得出最终效率。实证结果表明全部30个地区的技术创新效率均具有提升空间,该方法可以对所有区域进行充分排序且与传统仁慈型和进取型交叉效率方法相比更符合实际情况。     

基于前景理论和区间交叉效率的冷链物流投资决策方法

针对冷链物流投资决策问题,提出了一种基于前景理论和区间交叉效率的决策方法.考虑到决策者不同的心理偏好以及参考点在前景函数中的重要性,在前景理论与区间交叉效率融合的过程中,首先将参考点定义为区间数的形式,并且对于每个被评价的决策单元,选择区间交叉效率值中的最大值和最小值同时作为参考点,得到两个区间交叉效率前景值矩阵.进而,通过引入反映决策者态度的参数,将两个区间交叉效率前景值矩阵进行集成,通过对态度参数的调整可以反映不同乐观水平决策者的主观偏好.最后,将提出的决策应用于冷链物流企业投资决策的实例中,验证了该方法的有效性和适用性.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

Research efficiency analysis of China's university faculty members: A modified meta-frontier DEA approach

Traditionally, data envelopment analysis (DEA) requires all decision-making units (DMUs) to have similar characteristics and experiences within the same external conditions. In many cases, this assumption fails to hold, and thus, difficulties will be encountered to some extent when measuring efficiency with a standard DEA model. Ideally, the performance of DMUs with different characteristics could be examined using the DEA meta-frontier framework. However, some of these DMUs are mixed-type DMUs that may affiliate with more than one group. Furthermore, the total number of observations of these mixed-type DMUs is limited. This is one of the common problems when studies focus on faculty research performance in higher education institutions. In general, a faculty member is affiliated with a certain department, and if the departmental assessment policy is not suitable for faculty members who are involved in interdisciplinary research, their performance could be underestimated. Therefore, the proposed model is an extension of the DEA meta-frontier framework that can assess the performance of mixed-type DMUs by constructing the reference set without the same type of DMUs. In this paper, the scientific research efficiency of faculty members at the Inner Mongolia University is used as an example to provide a better understanding of the proposed model. The proposed model is intended to provide a fair and balanced performance assessment method that reflects actual performance, especially for mixed-type DMUs.     

A cautionary note on methods of comparing programmatic efficiency between two or more groups of DMUs in data envelopment analysis

In some applications of data envelopment analysis (DEA) there may be doubt as to whether all the DMUs form a single group with a common efficiency distribution. The Mann–Whitney rank statistic has been used to evaluate if two groups of DMUs come from a common efficiency distribution under the assumption of them sharing a common frontier and to test if the two groups have a common frontier. These procedures have subsequently been extended using the Kruskal–Wallis rank statistic to consider more than two groups. This technical note identifies problems with the second of these applications of both the Mann–Whitney and Kruskal–Wallis rank statistics. It also considers possible alternative methods of testing if groups have a common frontier, and the difficulties of disaggregating managerial and programmatic efficiency within a non-parametric framework.      

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.     

Visualizing Efficiency and Reference Relations in Data Envelopment Analysis with an Application to the Branches of a German Bank

The interest in Data Envelopment Analysis (DEA) as a method for analyzing the productivity of homogeneous Decision Making Units (DMUs) has significantly increased in recent years. One of the main goals of DEA is to measure for each DMU its production efficiency relative to the other DMUs under analysis. Apart from a relative efficiency score, DEA also provides reference DMUs for inefficient DMUs. An inefficient DMU has, in general, more than one reference DMU, and an efficient DMU may be a reference unit for a large number of inefficient DMUs. These reference and efficiency relations describe a net which connects efficient and inefficient DMUs. We visualize this net by applying Sammons mapping. Such a visualization provides a very compact representation of the respective reference and efficiency relations and it helps to identify for an inefficient DMU efficient DMUs respectively DMUs with a high efficiency score which have a similar structure and can therefore be used as models. Furthermore, it can also be applied to visualize potential outliers in a very efficient way.JEL Classification: C14, C61, D24, M2     

Performance evaluation using data envelopment analysis in the presence of time lags

Data Envelopment Analysis (DEA) is a methodology that computes efficiency values for decision making units (DMU) in a given period by comparing the outputs with the inputs. In many applications, inputs and outputs of DMUs are monitored over time. There might be a time lag between the consumption of inputs and the production of outputs. We develop an approach that aims to capture the time lag between the outputs and the inputs in assigning the efficiency values to DMUs. We propose using weight restrictions in conjunction with the model. Our computational results on randomly generated problems demonstrate that the developed approach works well under a large variety of experimental conditions. We also apply our approach on a real data set to evaluate research institutions.     

Hierarchies and Groups in DEA

Conventional applications of data envelopment analysis (DEA) presume the existence of a set of similar decision making units, wherein each unit is evaluated relative to other members of the set. Often, however, the DMUs fall naturally into groupings, giving rise first to the problem of how to view the groups themselves as DMUs, and second to the issue of how to deal with several different ratings for any given DMU when groupings can be formed in different ways. In the present paper we introduce the concept of hierarchical DEA, where efficiency can be viewed at various levels. We provide a means for adjusting the ratings of DMUs at one level to account for the ratings received by the groups (into which these DMUs fall) at a higher level. We also develop models for aggregating different ratings for a DMU arising from different possible groupings. An application of these models to a set of power plants is given.     

A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.     

Modelling generalized firms’ restructuring using inverse DEA

The key consideration for firms’ restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper.     

Integration of network data envelopment analysis and decision-making trial and evaluation laboratory for the performance evaluation of the financial holding companies in Taiwan

Data envelopment analysis (DEA) has been constantly used to measure the technical efficiency of decision-making units (DMUs). However, the major problem of traditional DEA methods is that they do not consider the possible intermediate effects. Recently, many papers have applied network DEA models to evaluate the efficiency scores. However, the linking activity of DMUs is still hard to be recognized. Hence, we employ DEMATEL to obtain the linking activity of DMUs. Our empirical research shows that the proposed method can soundly deal with the purpose of identifying the relationship between variables and derive the reasonable result in network DEA.