Assessing Technical Progress Via Data Envelopment Analysis

ＡｓｓｅｓｓｉｎｇＴｅｃｈｎｉｃａｌＰｒｏｇｒｅｓｓＶｉａＤａｔａＥｎｖｅｌｏｐｍｅｎｔＡｎａｌｙｓｉｓＬＩＧｕａｎａｊｉｎ；ＬＩＵＹｏｎｇｑｉｎｇ（ＡｕｔｏｍａｔｉｏｎＤｅｐｔ．，ＳｏｕｔｈＣｈｉｎａＵｎｉｖｅｒｓｉｔｙｏｆＴｅｃｈｎｏｌｏｇｙＧｕ...     

System Dynamics Model of Urban Water Resources Use and It's Application

ＳｙｓｔｅｍＤｙｎａｍｉｃｓＭｏｄｅｌｏｆＵｒｂａｎＷａｔｅｒＲｅｓｏｕｒｃｅｓＵｓｅａｎｄＩｔ＇ｓＡｐｐｌｉｃａｔｉｏｎＺＨＡＮＧＣｈａｏ（Ｄｅｐｔ．ｏｆＨｙｄｒａｕｌｉｃＥｎｇｉｎｅｅｒｉｎｇｏｆＴｓｉｎｇｈｕａＵｎｉｖ．，Ｂｅｉｊｉｎｇ１０００...     

Decision with Grey Correlation and AHP

ＤｅｃｉｓｉｏｎｗｉｔｈＧｒｅｙＣｏｒｒｅｌａｔｉｏｎａｎｄＡＨＰＺＨＡＪｉｎｇｍｉｎａｎｄＺＨＯＮＧＤｏｎｇｈｕａ（Ｄｅｐｔ．ｏｆＨｙｄｒａｕｌｉｃｉｃＥｎｇｉｎ．，ＴｉａｎｊｉｎＵｎｉｖｅｒｓｉｔｙ，Ｔｉａｎｊｉｎ３０００７２，Ｃｈｉｎａ）Ａｂｓ...     

Econometric Model in Qingchuan County

ＥｃｏｎｏｍｅｔｒｉｃＭｏｄｅｌｉｎＱｉｎｇｃｈｕａｎＣｏｕｎｔｙＸＵＪｉｕｐｉｎｇ；ＬＩＵＧｕａｎｇｚｈｏｕｇ；ＷＡＮＧＲｕｉ（Ｄｅｐｔ．ｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ＣｈｅｎｇｄｕＵｎｉｖｅｒｓｉｔｙｏｆＳｃｉｅｎｃｅａｎｄＴｅｃｈｎ...     

A Genetic Algorithm for Optimal Design of Model Following Control with Uncertain Disturbance

ＡＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｆｏｒＯｐｔｉｍａｌＤｅｓｉｇｎｏｆＭｏｄｅｌＦｏｌｏｗｉｎｇＣｏｎｔｒｏｌｗｉｔｈＵｎｃｅｒｔａｉｎＤｉｓｔｕｒｂａｎｃｅＲｅｃｅｉｖｅｄＡｐｒ．２１，１９９７ＺＨＡＮＧＸｉａｏｊｕｎＹＡＭＡＮＥＹｕｚｏａｎｄＹＡ...     

B-Assignment Problems

ＢＡｓｉｇｎｍｅｎｔＰｒｏｂｌｅｍｓＢＡＩＧｕｏｚｈｏｎｇＤｅｐｔ．ｏｆＭａｔｈ．，ＦｏｓｈａｎＵｎｉｖｅｒｓｉｔｙ，Ｇｕａｎｇｄｏｎｇ，５２８０００，ＣｈｉｎａＩｎｐｒａｃｔｉｃｅ，ｗｅｏｆｔｅｎｃｏｍｅａｃｒｏｓＡｓｓｉｇｎｍｅｎｔＰｒｏｂｌｅ...     

A Simulant Model of Development of Animal Husbandry and Application

ＡＳｉｍｕｌａｎｔＭｏｄｅｌｏｆＤｅｖｅｌｏｐｍｅｎｔｏｆＡｎｉｍａｌＨｕｓｂａｎｄｒｙａｎｄＡｐｐｌｉｃａｔｉｏｎＨＥＣｈａｎｇｚｈｅｎｇ；ＸＵＪｉｕｐｉｎｇ；ＬＩＵＧｕａｎｇｚｈｏｎｇ（Ｄｅｐｔ．ｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，Ｃｈｅｎ...     

Stochastic Factors Affecting the Diffusion of A Technological Inndovation-A

ＳｔｏｃｈａｓｔｉｃＦａｃｔｏｒｓＡｆｆｅｃｔｉｎｇｔｈｅＤｉｆｆｕｓｉｏｎｏｆＡＴｅｃｈｎｏｌｏｇｉｃａｌＩｎｎｄｏｖａｔｉｏｎ－ＡＳｙｓｔｅｍａｔｉｃＲｅｖｉｅｗＧＵＡＮＪｉａｎｃｈｅｎｇＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅｒｏ．Ａｓｔｒｏ...     

Grey Automata

１ＢａｓｉｃＤｅｆｉｎｉｔｉｏｎｓＬｅｔＵｂｅａｓｅｔ．ＡｇｒｅｙｓｕｂｓｅｔＧｏｆＵｉｓｃｈａｒａｃｔｅｒｉｚｅｄｂｙｔｈｅｆｏｌｏｗｉｎｇｍｅｍｂｅｒｓｈｉｐｆｕｎｃｔｉｏｎ：ｆＧ：Ｕ→［０，１］×［０，１］Ｆｏｒｘ∈Ｕ，ｆＧ（ｘ）＝＜μＧ（ｘ...     

Research of Population Development in Qingchuan County

ＲｅｓｅａｒｃｈｏｆＰｏｐｕｌａｔｉｏｎＤｅｖｅｌｏｐｍｅｎｔｉｎＱｉｎｇｃｈｕａｎＣｏｕｎｔｙＨＥＣｈａｎｇｚｈｅｎｇ；ＬＩＵＧｕａｎｇｚｈｏｎｇ；ＸＵＪｉｕｐｉｎｇ（Ｄｅｐｔ．ｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，ＣｈｅｎｇｄｕＵｎｉｖｅｒｓ...     

评价指标与DEA有效的关系

为使DEA评价结果具有合理的区分度,现有经验是使输入、输出指标总数与决策单元数之间满足一定的数量关系。通过对比研究输入、输出指标数之和、之积与DEA有效单元数之间的关系,对上述经验的合理性进行了探讨,得到了一些重要结论。     

Efficiency decomposition with shared inputs and outputs in two-stage DEA

Data envelopment analysis (DEA) is an effective non-parametric method for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and outputs. In many real situations, the internal structure of DMUs is a two-stage network process with shared inputs used in both stages and common outputs produced by the both stages. For example, hospitals have a two-stage network structure. Stage 1 consumes resources such as information technology system, plant, equipment and admin personnel to generate outputs such as medical records, laundry and housekeeping. Stage 2 consumes the same set of resources used by stage 1 (named shared inputs) and the outputs generated by stage 1 (named intermediate measures) to provide patient services. Besides, some of outputs, for instance, patient satisfaction degrees, are generated by the two individual stages together (named shared outputs). Since some of shared inputs and outputs are hard split up and allocated to each individual stage, it needs to develop two-stage DEA methods for evaluating the performance of two-stage network processes in such problems. This paper extends the centralized model to measure the DEA efficiency of the two-stage process with non splittable shared inputs and outputs. A weighted additive approach is used to combine the two individual stages. Moreover, additive efficiency decomposition models are developed to simultaneously evaluate the maximal and the minimal achievable efficiencies for the individual stages. Finally, an example of 17 city branches of China Construction Bank in Anhui Province is employed to illustrate the proposed approach.     

基于竞争决策单元的数据包络分析模型

决策单元之间处于竞争关系时,所有决策单元的某些投入或产出总和是有限的,一个决策单元改善自身效率的行为可能会影响其他决策单元.针对此情形,提出一种基于竞争决策单元的数据包络分析模型,比较了该模型与传统DEA模型的生产前沿面的区别,并指出此模型在管理决策领域可能的用途.最后,用一个算例分析演示了此模型.     

A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS

The traditional data envelopment analysis(DEA)model can evaluate the relative efficiencies of a set of decision making units(DMUs)with exact values of inputs and outputs,but it cannot handle imprecise data.Imprecise data,for example,can be expressed in the form of the interval data or mixtures of interval data and ordinal data.In this study,a cross-efficiency method is introduced into the DEA model to calculate the interval of cross-efficiency values,based on which a new TOPSIS method is proposed to rank the DMUs.Two examples are presented to illustrate and validate the proposed method.     

数据包络分析的灵敏度研究及其应用

研究并给出数据包络分析中ＣＣＲ模型的一种灵敏度分析方法，提出有效决策单元在输入／输出变动时，保持有效性不变的充分条件。实用表明，该法稳定有效。     

基于误差传递和熵的区间DEA方法

传统的DEA(data envelopment analysis)模型只能用来处理精确数据,而在现实生活中,由于统计误差、信息不完全等原因,获取精确数据十分困难,数据往往只能以区间数的形式给出.因此,如何在不确定条件下利用DEA方法评价决策单元的相对效率一直是DEA理论的研究前沿.针对这一问题,文章在DEA传统模型的基础上,利用改进的交叉效率思想,使用误差传递和熵处理区间数据,求得每个决策单元全局交叉效率的误差分布形式(中点值和误差最大估计值),然后根据全局交叉效率,使用有向距离指数给决策单元排序.最后通过两个算例来说明本文提出的方法的可行性和有效性.     

基于期望值方法的随机DEA综合模型及应用

对环境约束下包含不可控输入、非期望输出的DEA综合模型的相对有效性进行研究．给出决策单元有效的定义及定理。进一步提出了随机条件下决策单元的期望有效性指数概念和相对有效性评价的期望值方法，最后给出了计算实例。     

DEA扩展有效的鲁棒性

通过给出决策单元为DEA扩展有效的几个等价命题，来研究扩展有效决策单元的鲁棒性。得到了当输入增加或输出减少后，它仍然保持扩展有效性的一些充分必要条件，还给出了保持扩展有效的判断方法和数值实例，而该方法不需要求文献[3]中的最优基矩阵的逆矩阵，也不需要求解文献[5]中的线性方程组。     

A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

DEA(data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure(RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model,especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA mode with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China's regional transportation systems(RTSs) considering the number of transport accidents(an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.     

具有非期望输出决策单元相对有效性评价的DEA模型

从生产可能集入手，将非期望输出直接反映到生产可能集中，并根据有效性的原理将决策单元的非期望输出和期望输出一起来进行相对有效性分析，并得到相应的面向输出的ＤＥＡ评价模型．