基于组合评价的长三角城市基础设施投资绩效研究

城市基础设施投资绩效的合理评价是进行科学投资决策的重要依据。为了对长三角25个城市基础设施投资绩效进行客观、合理的评价,建立了基础设施投资绩效评价指标体系,选取2009年截面数据采用组合评价法进行综合评价。该方法首先使用超效率DEA、熵权TOPSIS、因子分析三种方法得出各自结果下的排名,在验证了各评价方法具有一致性的前提下采用模糊Borda综合评价法得出各城市的最终排名。组合评价方法弥补了单一方法的不足,有助于得到更客观合理的评价结果,为有关城市和部门把握基础设施投资绩效和未来决策提供参考。     

模糊条件下的决策单元相对有效性评价

研究了模糊条件下决策单元的相对有效性评价问题。首先分析了模糊性因素对决策单元相对有效性的影响;然后根据模糊规划取截集方法和DEA评价的经济含义,给出了模糊DEA模型的求解方法;最后定义了决策单元的模糊DEA有效性以及进行有效性排序的平均置信有效性。文末是一个模糊DEA应用的例子。     

具有偏好锥的链式网络DEA模型

对链式网络DEA模型进行推广,将"偏好锥"引入网络DEA模型.针对中间产出重要性以及决策者评价时的偏好,建立带有产出锥和投入锥相应的两阶段生产可能集,对具有"偏好锥"的链式网络DEA模型,证明了决策单元为网络DEA有效的充要条件,给出了网络DEA有效性与各阶段弱DEA有效性的关系.另外,文章结合具体算例说明了偏好锥的变化对效率评价的影响.关于两阶段的模型以及相关结论可以推广到多阶段网络结构.     

关联网络DEA在多层次指标体系中的应用研究

对复杂系统进行效率评价,是改善其运作性能的必要之举。DEA作为一种典型的效率评价方法,现有研究已提出了若干串联、并联系统的DEA效率评价问题。然而,为评价复杂系统的效率,建立的评价指标体系往往具有多层次性,常有串并联混合结构出现。现有研究囿于将复杂系统中的层次关系作为黑箱处理,或者只研究串联或并联系统,没有有效考察DEA在串并联混合结构中效率评价的应用,对复杂系统的"多层次指标体系"不能进行有效评价。与传统的DEA评价方法相比,本文首次将网络DEA引入多层次指标体系的评价当中,以串并联混合结构为基础,构建了用于多层次指标体系评价的关联网络DEA模型;从模型创新的角度,本文的关联网络DEA模型融合了串联和并联系统DEA模型,为处理子系统间复杂的关联关系提供了新思路。最后,通过对军民结合企业的实证研究验证了模型的合理性。     

基于网络DEA的供应链效率评价

数据包络分析(DEA)是评价决策单元(DMU)相对有效性的一种重要的工具.供应链效率评价是供应链管理的重要内容,对于衡量供应链目标的实现程度及提供经营决策都具有十分重要的意义.DEA是用于评价供应链效率的一种有效的方法,但是传统的DEA不考虑供应链的内部结构,对系统的相对效率评价偏高;而研究具有串联结构的供应链,考虑把部分中间产品作为最终产品输出,并增加额外中间投入的情形.基于此,建立相应的网络DEA模型,进行了相关理论的研究,给出了弱网络DEA有效的充分条件,以及整个系统弱网络DEA有效与各个子系统网络DEA有效的关系等.     

双准则广义DEA模型及其模糊解法

针对单准则广义DEA模型在评价决策单元有效性时的局限性,从输入和输出两个角度同时考虑,建立了双准则广义DEA模型,并应用模糊数学理论给出其求解方法.     

基于数据包络分析的农业循环经济评价——以黑龙江省各地区为例

基于农业循环经济相关理论,从农业资源投入和经济效益产出的技术经济出发构建农业循环经济评价指标体系,采用DEA方法建模和"投影"方法优化,对黑龙江省13个地区农业循环经济的DEA.有效性和规模收益情况进行了实证分析.结果显示:黑龙江省农业循环经济的DEA.有效有11个地区;非DEA有效有2个地区并经"投影"优化出DEA有效的决策方案.     

只有输出(入)的DEA改进模型及其应用

研究了只有输出(入)的DEA分析方法,针对只有输出(入)DEA模型的不足,重新定义了只有输出(入)的DEA评价方法的有效性,并改进了模型。相对已有的只有输出(入)的DEA模型,该模型充分利用了决策单元的诸输出(入),提高了DEA评价的效果。作为应用,运用新模型对武警防暴队形优选问题进行了有效性分析。     

基于FOA-RBF神经网络的外贸出口预测

应用果蝇优化算法对径向基神经网络扩展参数的优化方法进行研究,给出了一种以标准误差计算公式为味道判定函数,以此确定最优的径向基函数的扩展参数值的方法,并建立了相应的预测模型.应用该预测模型对黑龙江省外贸出口额进行预测,结果表明:预测模型的预测精度优于径向基神经网络,从而证明了方法的有效性.     

基于样本评价的广义数据包络分析方法

传统DEA方法是一种依据自评体系评价的方法,而无法自主选择参照系.为了解决DEA方法可以同时依据自评体系和其它参照系进行评价问题,首先给出了广义DEA有效的概念.然后,给出了一类基于样本单元评价的广义数据包络分析模型,包括面向输入的广义DEA模型、面向输出的广义DEA模型以及加性广义DEA模型.最后,分析了上述这些模型与传统DEA模型之间的关系,探讨了广义DEA有效与相应多目标规划Pareto有效之间的关系,并给出了决策单元的投影性质以及决策单元的有效性排序方法.     

中国铁路路网自然垄断属性及其管制研究

以网运分离的视角,基于DEA(data envelopment analysis)数据包络分析模型,运用1993~2016年相关成本与产出指标数据,对中国铁路行业路网环节的自然垄断属性进行了实证分析。结果表明:中国铁路行业路网环节在实证研究的时间段内不存在规模报酬递增,因此不具有明显的自然垄断属性。由此,中国铁路行业路网环节的政府管制需要改革,不仅包括准入和价格管制的放松,还有适度的投资管制以及质量管制的加强。     

Enhancement of equity portfolio performance using data envelopment analysis

This paper examines the applicability of data envelopment analysis (DEA) as a basis of selection criteria for equity portfolios. It is the first DEA application for constructing a combined equity investment strategy that aims to integrate the benefits of both value investing and momentum investing. The 3-quantile portfolios are composed of a comprehensive sample of Finnish non-financial stocks based on their DEA efficiency scores that are calculated using three variants of DEA models (the constant returns-to-scale, the super-efficiency, and the cross-efficiency models). The performance of portfolios is evaluated on the basis of the average return and several risk-adjusted performance metrics throughout the 1994–2010 sample period.     

陆上油田开发投资规模方法研究

本介绍一种计算油田开发经济投资规模的方法，中从技术和经济角度出发，用聚类分析方法对油田进行分类，利用DESA有效性原理，建立投资规模界限值数学模型，采用数学模型研究石油勘探开发经济投资规模，为投资决策提供定量依据。     

基于关联网络DEA的供应链协调程度评价

本文给出了一种供应链网络协调程度的评价方法。研究思路是:首先,关联网络DEA能求出供应链网络上各个决策单元的DEA效率。其次,假设供应链网络实现了协调,那么网络上所有企业的DEA效率应该都为1。因此可以用供应链网络的实际DEA效率与协调状态(即所有决策单元DEA效率均为1)的差距,来评价供应链网络的协调程度。基于这一思路,本文研究了一个由3个供应商、2个制造商和3个零售商组成的供应链网络的协调评价问题,构建了基于关联网络DEA的类方差协调评价模型,实现了对供应链效率和协调性的同时测量,并通过数值算例验证了方法的实用性和有效性。     

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment.     

Benchmark optimization and attribute identification for improvement of container terminals

The aim of this paper is to optimize the benchmarks and prioritize the variables of decision-making units (DMUs) in data envelopment analysis (DEA) model. In DEA, there is no scope to differentiate and identify threats for efficient DMUs from the inefficient set. Although benchmarks in DEA allow for identification of targets for improvement, it does not prioritize targets or prescribe level-wise improvement path for inefficient units. This paper presents a decision tree based DEA model to enhance the capability and flexibility of classical DEA. The approach is illustrated through its application to container port industry. The method proceeds by construction of multiple efficient frontiers to identify threats for efficient/inefficient DMUs, provide level-wise reference set for inefficient terminals and diagnose the factors that differentiate the performance of inefficient DMUs. It is followed by identification of significant attributes crucial for improvement in different performance levels. The application of this approach will enable decision makers to identify threats and opportunities facing their business and to improve inefficient units relative to their maximum capacity. In addition, it will help them to make intelligent investment on target factors that can improve their firms’ productivity.     

DEA与DRF法的整合

本文针对利用DEA模型进行有效性评价时碰到问题，提出了用因子分析法（简称DRF法）与DEA法相结合来进行评价，这是对DEA法的一种改进。并在此基础上对医院绩效进行了评价，为财政部门科学制定《医疗单位财政支出效益评价实施办法》提供依据。     

Hedge fund performance appraisal using data envelopment analysis

In this paper we apply data envelopment analysis (DEA) to evaluate the performance of hedge fund classifications. The purpose of alternative investment strategies such as hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance could be ineffective. With the increasing number of hedge funds available, institutional investors, pension funds, and high net worth individuals urgently need a trustworthy efficiency appraisal method. DEA can achieve this. An important benefit of the DEA measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal distribution of hedge fund returns. We suggest that DEA be used as a complimentary technique (or method) for the selection of efficient hedge funds and funds of hedge funds for investors. Using DEA can shed light and further validate hedge fund manager selection with other methodologies.