基于模糊期望值的模糊DEA 交叉效率评价方法

基于模糊DEA的交叉效率评价方法研究是一个崭新的研究课题,有着广阔的应用前景。结合基于模糊期望值的模糊 DEA 模型和交叉效率原理,提出一种新的交叉效率的评价方法。该方法首先求出基于模糊期望值的最优效率值权重,然后由这组模糊最优权重求解他评效率并构造交叉效率矩阵,最后根据求出的模糊期望交叉效率值对各DMU进行排序。     

基于模糊评价方法的网络安全评价研究

针对网络安全性评价研究中的度量问题建立了基于ISO/IEC9126标准的网络安全指标体系模型.结合网络安全评价方法研究中的模糊性和不确定性等常见问题,首先以度量数据和各项指标计算公式为基础计算其指标值.其次,运用指标体系模型和模糊评价方法对评价指标进行模糊化处理.最后,结合各指标评价质量和模糊综合评价方法评价网络安全性.通过建立网络安全性评价模型,有效地解决了网络安全性的多指标评价问题,可用于指导相关人员进行网络安全方面改进.     

网管系统的性能模糊综合评价模型

针对网管系统性能评价研究中的度量问题,运用性能指标和模糊数学方法对性能评价进行模糊化处理,以度量的性能数据为基础,采用模糊综合评价方法评价质量因子,通过建立性能综合评价模型,有效地解决了多指标评价问题,可用于指导软件开发人员进行网管系统性能改进。     

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.     

运用模糊综合评判理论评价软件质量

为了对软件开发的质量进行有效的识别和评价,可以通过对软件开发中质量特性的分析和归纳,建立模糊层次综合特性评价模型,运用层次分析法确立软件质量的各因素的权重,得到评价矩阵并对其做模糊化处理,然后通过运算得到综合评价结果。本文对有些情况下单层模糊综合评判得不出评判结果最大隶属度的问题,可以采取双层模糊综合评判来得出最大隶属度的问题进行讨论,从而为软件质量控制以及评价提供了科学依据。     

基于证据理论-DEA 交叉效率的混合型多属性决策方法

针对含有投入产出指标的混合型多属性决策问题,提出一种基于证据理论和数据包络分析(DEA)交叉效率的决策方法.首先运用DEA对决策系统中投入产出指标进行处理,得到DEA交叉效率矩阵,并运用证据理论集结其交叉效率得分;然后将效率得分作为决策系统指标值,与系统中其他指标进行模糊等级转换,通过证据理论对指标值融合,进而得到决策单元的期望效用,据此对决策单元进行排序;最后通过实例与其他文献方法进行对比分析,以表明所提出方法的可行性和有效性.     

基于决策单元异质性的群组交叉效率评价方法

交叉效率评价方法是数据包络分析（DEA）的拓展工具,但是现存交叉效率方法是对所有决策单元（DMUs）统一测评,没有考虑决策单元间的异质性问题。提出了一种考虑决策单元异质性的群组交叉效率模型,将具有异质性的群组间效率值进行合理集结的绩效评价方法。运用仁慈型交叉效率模型分析各群组内部的相对效率值;运用改进的熵权法为群组内部各决策单元分配适当权重,得到群组整体的最优权重向量;运用传统交叉效率模型评价与分析群组之间的相对效率值,并以此进行综合排序。为证明该方法具有理论与适用效力,2015年应用于16家中国商业银行的绩效评价,结果表明该方法行之有效。     

煤矿电能质量综合量化评价研究

根据煤矿用电负荷的特殊要求,定义了以最大负荷电流为基准的电流总畸变率,以及考虑负荷电流波形非正弦性的畸变功率因数;从电压质量、电流质量和功率因数3个方面确定电能质量分项指标,采用最小均方差法对电能分项指标进行筛选,建立了适用于煤矿供电系统的电能质量综合评价指标体系。运用熵权法计算不同指标的相对重要性,通过概率统计与模糊数学相结合的方法构建电能质量综合量化评价模型,实现对煤矿电网电能质量的分项计算和综合评价。实测数据分析结果验证了所构建的煤矿电能质量综合评价指标体系的合理性和评价方法的实用性。     

基于模糊-两阶段超效率SBM的电网应急能力动态综合评价

首先,对电网应急能力进行基于时间维度和空间维度的拆解,构建基本电网应急能力评价指标体系;其次,考虑到指标评价标准具有动态性的问题,采用三角模糊数对指标进行量化处理,并建立可对多个模糊数集结的优化模型;然后,采用α-截集对三角模糊数进行处理并建立基于单一时间节点下的模糊-两阶段超效率SBM模型实现对电网应急能力的静态评价;最后,以时间维度拆解为基础,建立电网应急能力动态综合评价模型,实现对应急能力的整体评价、排序,使评价结果更加具有科学性和合理性.实例分析结果表明,所提出方法可有效区分应急能力仍需作出改善的区域,并在一定程度上克服评价标准需要动态变化的问题,具有较强的客观性.     

局部模糊综合评价方法及其在健康普适服务中的应用*

提出一种基于社区健康指数的服务质量评价模型。该模型采用局部模糊的综合评价法,根据指标性质灵活采用模糊评价和综合评价,减少了评价指标的数据丢失;此外,采用主客观结合的方式,自顶向下确定指标的权重,增强了权重值的可信度。实验证明该模型提高了评价的准确性,具有很强的实用价值。     

Minimax and maximin formulations of cross-efficiency in DEA

In the conventional cross-efficiency formulation, the efficiency score of a DMU under evaluation is maximized as the primary goal while the average cross-efficiency of peer DMUs is minimized (or maximized) as the secondary goal. The proposed models replace the secondary goal with the minimization (or maximization) of the best (or worst) cross-efficiency of peer DMUs. We demonstrate the appropriateness of the proposed formulations of cross-efficiency for certain efficiency evaluation contexts, and show how they help enhance the usefulness of cross-efficiency evaluation in DEA using a randomly generated sample data set. For a solution method for the proposed models of cross-efficiency, we develop a bisection algorithm whose computational complexity is polynomial.     

A learning-based comprehensive evaluation model for traffic data quality in intelligent transportation systems

Human motion modelling has attracted more and more attentions in various industrial fields with the event of information technology. Previous studies focus on capturing, animating, understanding and modelling human gestures or physical activities. However, in many applications such as Intelligent Transportation Systems (ITS), the traffic data quality (TDQ) is becoming a critical issue which can has great influence on the efficiency of the modelling. In this paper, we focus on evaluating the traffic data quality (TDQ) from the large amount of detectors and traffic flow data in the modelling of Intelligent Transportation Systems (ITS). We first introduce four error indices of an occupancy speed model and an occupancy flow model as model evaluation indices, and two indices from experts as non-model evaluation indices. Then, we propose a comprehensive evaluation model (CEM) for TDQ. Furthermore, we develop two algorithms for training the parameters in CEM based on the least square method (LSM) and the adaptive network based fuzzy inference system (ANFIS). We compare the proposed algorithms with the real-world traffic flow data which has been collected on Beijing ring-roads and connected lines. The experimental results show that the ANFIS-based learning method outperforms in most scenarios and ensures the evaluation error less than 10 %, which can significantly improve the efficiency of identifying traffic flow detectors with low data quality.     

A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty

The paper presents a novel cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique for evaluating different elements (Decision Making Units or DMUs) under uncertainty. In order to evaluate the performance of several DMUs while dealing with uncertain input and output data, the presented technique employs triangular fuzzy numbers. A fuzzy triangular efficiency is associated to each DMU through a cross evaluation obtained by a compromise between suitably chosen objectives. Results are then defuzzified to provide a ranking of the DMUs. The proposed method is applied to the performance evaluation of healthcare systems in a region of Southern Italy. The DMU data uncertainty derives from ongoing reforms and the reported assessment is conducted firstly in order to evaluate and rank the efficiency of the considered healthcare systems, and subsequently to assess the evolution of the performance of one of the most affected among these DMUs by the reform plans. The case study demonstrates the model ease of application, its discriminative power among DMUs when compared to a more classical fuzzy DEA approach, and the usefulness in planning and validating targeted reforms in the case of healthcare systems.     

通用准则评估综述

在介绍CC国内外发展现状的基础上，系统地分析了通用准则CC的基本概念、主要思想以及CC评估角色和类型等主要问题，并对CC评估配套方法——CEM及其评估流程进行了深入探讨，最后在总结CC评估现状的基础上，指出了CC评估所存在的问题及其未来的发展趋势。     

间歇性风电并网能源调度综合模糊评价系统设计

针对传统风电并网能源调度评价系统设计评估准确率低、系统响应时间慢的不足,提出基于综合模糊理论的能源调度评价系统设计。给出了模糊评价系统的硬件架构与软件实现流程,依托于数据输入管理模块、编辑查询模块、模糊评价模块与数据输入模块构建了可变模糊评价模型,并分别确定出两级的指标评价体系、权重指标和模糊隶属度矩阵;基于模糊算子和模型参数确定出综合评价向量矩阵,实现对并网能源调度方案的精确评价。算例分析结果表明,提出评价系统的平均偏差率可以控制到0.131%,显著优于传统能源调度评价系统。     

E-learning系统中学习评价的研究

针对目前E-learning系统中不能全面地对学员的学习情况进行评价的问题,提出了一个基于模糊综合评判法的E-learning学习评价综合评判模型,并用C语言对所提出的模型进行了程序实现。把学生的学习数据及运行结果进行对比分析可知:模糊综合评判法能综合考虑学员学习过程中表现出的各种因素,从而能得到更全面、更准确的评价结果。     

Weight determination in the cross-efficiency evaluation

A neutral data envelopment analysis (DEA) model for cross-efficiency evaluation was recently proposed by Wang and Chin [Wang and Chin (2010b). A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications, 37(5), 3666–3675], which maximinimizes the relative efficiency of each output and effectively reduces the number of zero weights of outputs. Since a large number of zero weights may still exist among inputs, this paper proposes a simultaneously input- and output-oriented weight determination DEA model for the cross-efficiency evaluation. The new DEA model proves to reduce the number of zero weights for both inputs and outputs very significantly, as illustrated by numerical examples. The weights determined by the new DEA model are neutral, neither aggressive nor benevolent.     

Expansion of the cross-efficiency evaluation by using the equations forming the efficient frontier in DEA

In this paper, we propose an algorithm to calculate cross-efficiency scores which used the equations forming the efficient frontier in data envelopment analysis (DEA). In many standard DEA models, each decision-making unit (DMU) is evaluated by using the advantageous weight for itself. Then, many DMUs are evaluated as efficient, and those efficient DMUs are not ranked by the models. The cross-efficiency evaluation is a method to rank DMUs by using the advantageous weights for all DMUs. Previously, the cross-efficiency scores based on different ideas are calculated by solving multiple linear or nonlinear programming problems. However, it is often hard to solve such a nonlinear programming problem. Therefore, by analysing the efficient frontier, we construct an algorithm to calculate alternative cross-efficiency scores.     

基于多级模糊评判的高效数据聚集算法

基于多级模糊综合评判,提出一种既可节省传感节点能量又能提高数据聚集度和减少数据传输延迟的高效的数据聚集算法.先由一级模糊综合评判获得路由信息,然后根据路由表中相邻传感节点转发的数据包个数,利用二级模糊综合评判重新选择下一跳传感节点,通过增加原有路径间交叠的方式来高效的提高数据聚集度;然后为了均衡能耗和减少传输延迟提出一种新的聚集定时机制.仿真结果表明,该算法能延长网络生命周期,提高数据包交付率,降低平均传输延迟及提升数据聚集度.