基于煤炭企业综合效益评价的数据包络分析模型研究

提出了基于DEA的煤炭企业综合效益评价模型,克服了煤炭企业系统多投入、多产出情况复杂不易评价的难题,如评价指标权重设定困难、评价函数未知时难于做出正确的判断等。论文采用DEA的C2R模型与C2GS模型为理论基础,对八家煤炭企业的综合效益进行了实证分析与评价,并给出了相应建议。     

基于数据包络分析的虚拟企业知识评价方法

业知识对企业性能、竞争力有着重要的影响,通过对伙伴企业知识的评价,可以加强虚拟企业对知识的管理,从而提高虚拟企业性能。针对虚拟企业知识评价的问题,提出一种以企业模型为媒介的间接的知识评价方法——KP²RP,并结合它的五个元素：知识、产品、过程、资源、性能,定义了它们之间的关联矩阵,给出了关联矩阵的知识评价级别,最后提出基于数据包络分析的知识评价模型,并且利用实例分析了评价方法的可行性。     

基于数据包络分析的软件任务性能基准评价

提出了一种基于数据包络分析的软件任务性能基准评价新方法——TaskBeD.介绍了TaskBeD的任务基准评价模型和核心算法(挖掘高性能的软件任务,建立参考任务集和结果的敏感度分析).实验结果显示,TaskBeD能够高效处理多变元和可变规模收益任务数据.     

基于数据包络和数据挖掘的财务危机预测模型研究

以往的企业财务危机预测研究只能预测企业是否具有财务危机,无法预测企业财务危机的程度,这是由于在界定企业财务危机时,只依据了企业是否为ST企业的分类方式。鉴于此,通过数据包络分析法,近一步细化了企业财务危机的分类,再使用关联规则算法筛选出重要的预测变量,最后使用决策树技术构建企业财务危机预测模型,并对分类的有效性和预测的准确率进行了验证。实证结果表明,基于数据包络和数据挖掘的财务危机预测模型既能保持较高的准确率,又能预测企业财务危机的程度,使得预测结果更具有参考价值。     

基于数据包络分析与ccr模型的病重成本自动计算系统

针对传统系统软件读写时间延长,系统吞吐性能下降的趋势,设计了基于数据包络分析与ccr模型的病重成本自动计算系统。调整ARM外设接口和FPGA外围接口。采用数据包络分析法,通过设置ccr模型决策条件,评价病重费用使用效率实现系统对病重成本的自动计算。实验结果表明：当数据读写时间超过120s时,新系统吞吐性能未改变,传统系统的吞吐性能出现下降趋势;当数据读写时间超过520s,传统系统吞吐性能快速下降,比新系统的吞吐性能分别低了2227msg/sec以及2222msg/sec。可见数据包络分析与ccr模型提高了系统在长时间工作时数据的吞吐能力。     

基于逆数据包络分析法的中医方药评价与药量计算研究

针对中医组方的药量推算建模问题,提出了基于证候上下文分析的逆数据包络分析（IDEA）算法。该算法以证侯的典型方剂为依据,以数据包络分析为评价工具,建立起反映证候演化的方药评价模型;依据代表证候状态的评价值,给相应的证型症状信息赋值,以完成“词计算”过程。最后,把具体的辨证过程植入到证侯发展的上下文环境中,以典型证型的“词计算”为依据,给出具体案例的证侯状态评价,再采用IDEA算法推算出药量数据。该模型的推算结果表明,它能够反映中医的一般用药规律,这将对辨证论治组方环节的数值化、客观化研究提供一种可能的思路。     

智能数据可视分析技术综述

如何从海量数据中快速有效地挖掘出有价值的信息以更好地指导决策,是大数据分析的重要目标.可视分析是一种重要的大数据分析方法,它利用人类视觉感知特性,使用可视化图表直观呈现复杂数据中蕴含的规律,并支持以人为本的交互式数据分析.然而,可视分析仍然面临着许多挑战,例如数据准备代价高、交互响应高延迟、可视分析高门槛和交互模式效率低.为应对这些挑战,研究者从数据管理、人工智能等视角出发,提出一系列方法以优化可视分析系统的人机协作模式和提高系统的智能化程度.系统性地梳理、分析和总结这些方法,提出智能数据可视分析的基本概念和关键技术框架.然后,在该框架下,综述和分析国内外面向可视分析的数据准备、智能数据可视化、高效可视分析和智能可视分析接口的研究进展.最后,展望智能数据可视分析的未来发展趋势.     

基于改进数据包络分析的高速列车开行方案的动态评价

针对数据包络分析(DEA)方法不能反映评价指标间权重的差异性以及不能对有效决策单元排序和调整的缺点,提出一种改进的DEA方法.首先, 运用层次分析法确定各指标的权重并建立偏好锥模型;然后, 运用交叉效率对所有决策单元进行排序并根据上座率和理想决策单元对部分决策单元进行调整; 最后,运用该方法对京沪高速列车开行方案进行评价.研究发现6条运行线中有4条是DEA有效的,需要对2条非有效和1条有效运行线进行调整.实验结果表明,改进的DEA方法能够为高速旅客列车开行方案的动态调整提供理论依据.     

二维雷达包络数据可视化及再提取技术

雷达信息可视化是现代战场可视化中最重要的环节之一,它对于整个战场的综合态势的把控有着至关重要的作用。针对二维多雷达包络数据可视化后数据丢失的问题,本文提出雷达包络检测算法对多雷达包络线进行提取,该算法可以准确地找回每段边界线对应的原始数据。另外,针对多雷达包络线显示慢的问题,本文采用位图技术进行包络线快速绘制。实验结果表明该方法可以快速更新多雷达包络线。     

基于OpenMP的包络对齐并行方法研究与实现

采用加矩形窗的积累互相关法和基于Fourier变换频域移位性质的最小熵法进行一维距离像包络对齐。针对包络对齐算法数据量大、复杂度高、运行时间长等缺点,提出一种应用于多核处理器的包络对齐并行算法。该方法利用OpenMP编译指导指令#pragma omp section和#pragma omp for对积累互相关算法和最小熵算法进行多线程并行优化。理论分析和仿真实验表明,该方法大大提升了算法的执行效率。     

A study of the relative efficiency of Chinese ports: a financial ratio‐based data envelopment analysis approach

Abstract: This research examines the relative efficiency of 11 major Chinese ports by using an innovative adopted version of Data Envelopment Analysis (DEA). DEA is a non‐parametric approach to weigh the inputs/outputs and measure the relative efficiency of decision‐making units. This paper adopts an output‐oriented version of DEA based on financial ratios in which no inputs are utilized. The new adopted DEA model provides a rounded judgement on port efficiency taking into consideration multiple financial ratios simultaneously and combining them into a single measure of efficiency. The mathematical model is solved for every port, and the relative efficiency of each port is determined. The results of DEA show that the higher a port's efficiency ratio in relation to the corresponding ratio of another port, the higher the efficiency of this port. Finally, suggestions based on the data analysis are provided for managerial decision makers to improve the areas needed for port operating efficiency.     

Fuzzy data envelopment analysis: A discrete approach

Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of “local α-level” to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.     

Two-stage inference using data envelopment analysis efficiency measurements in univariate production models

This article addresses the problem of modeling data envelopment analysis (DEA) inefficiencies as dependent on contextual variables. For this purpose we use a statistical model similar in appearance to inefficiency component specifications in stochastic frontier models. The underlying production response is univariate. The approach is asymptotic and is based on a two‐stage statistical inference procedure. In the first stage inefficiencies are estimated using DEA. In the second stage these estimates are modeled as if they were the true inefficiencies by means of a statistical model dependent on the contextual variables. To define this data generating process one could use a flexible family of distributions like the truncated normal. Theoretical inefficiencies are assumed to be independent but not identically distributed. Some of the asymptotic results implied by the two‐stage inference procedure are inspected in finite samples by means of Monte Carlo simulations. The procedure is illustrated with an example where a deterministic production model is fitted to research data generated by the major state company responsible for agricultural research in Brazil.     

Slacks-based measures of efficiency in imprecise data envelopment analysis: An approach based on data envelopment analysis with double frontiers

Data envelopment analysis (DEA) is a mathematical approach for evaluating the efficiency of decision-making units (DMUs) that convert multiple inputs into multiple outputs. Traditional DEA models assume that all input and output data are known exactly. In many situations, however, some inputs and/or outputs take imprecise data. In this paper, we present optimistic and pessimistic perspectives for obtaining an efficiency evaluation for the DMU under consideration with imprecise data. Additionally, slacks-based measures of efficiency are used for direct assessment of efficiency in the presence of imprecise data with slack values. Finally, the geometric average of the two efficiency values is used to determine the DMU with the best performance. A ranking approach based on degree of preference is used for ranking the efficiency intervals of the DMUs. Two numerical examples are used to show the application of the proposed DEA approach.     

Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis

In the last decade,ranking units in data envelopment analysis(DEA) has become the interests of many DEA researchers and a variety of models were developed to rank units with multiple inputs and multiple outputs.These performance factors(inputs and outputs) are classified into two groups:desirable and undesirable.Obviously,undesirable factors in production process should be reduced to improve the performance.Also,some of these data may be known only in terms of ordinal relations.While the models developed in the past are interesting and meaningful,they didn t consider both undesirable and ordinal factors at the same time.In this research,we develop an evaluating model and a ranking model to overcome some deficiencies in the earlier models.This paper incorporates undesirable and ordinal data in DEA and discusses the efficiency evaluation and ranking of decision making units(DMUs) with undesirable and ordinal data.For this purpose,we transform the ordinal data into definite data,and then we consider each undesirable input and output as desirable output and input,respectively.Finally,an application that shows the capability of the proposed method is illustrated.     

Ranking alternatives in a preferential voting system using fuzzy concepts and data envelopment analysis

In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.     

Sustainability assessment of Iranian petrochemical companies in stock exchange: A data envelopment analysis-based approach

The assessment of efficiency is always of particular importance according to different indicators from different perspectives. There are various techniques for evaluating petrochemical companies, among which the data envelopment analysis technique is one of the best techniques that can be used to calculate the relative efficiency of a set of decision-making units with network structures. In the present paper, seven petrochemical companies listed in the Iranian stock exchange were analysed. These companies were evaluated in terms of financial performance and sustainable development, and their relative efficiency was calculated during 2015–2016. According to the obtained results, only Marun Petrochemical Co. was found to be efficient in all areas and years. The results also showed that four companies were efficient in financial terms over the period under study. In the general conclusion regarding the companies' performance, Marun was ranked first, Jam was ranked second, and Zagros was ranked third.     

Indian thermal power plant challenges and remedies via application of modified data envelopment analysis

This paper evaluates the performance of coal‐fired thermal power plants in India for the year 2008–2009 using data envelopment analysis (DEA); subdividing the power plants into three categories depending on their scale—small, medium, and large. The classical DEA model is analyzed to identify the efficient ones from the whole gamut of plants run by various organizations of the central government, state government, and private sector. Slack analysis is carried out to explore the specific areas that need to be focused on, in quantitative terms, for the overall efficiency improvement. Further efficiency evaluation is extended from a single criterion‐based conventional approach to a multiple criteria oriented approach, and the resulting DEA models are more efficient and flexible in many aspects, particularly in discriminant and weight analysis. Results of multicriteria DEA (MCDEA) are substantiated with cross‐efficiency analysis by deploying the weights obtained by the MCDEA in the cross‐efficiency analysis. On the basis of the insights provided by the outcome of the analysis, both qualitative and quantitative measures are proposed for improvement of the plant performances. The result of this analysis may assist the management of the power plants to introspect and review their systems and processes for optimal use of resources. The methodology adopted in the present work can also be employed for deeper understanding of power plants in other parts of India as well as in other countries.     

An interactive application of data envelopment analysis in microcomputers

This article describes a general-purpose microcomputer code for data envelopment analysis (DEA) that incorporates four different DEA models in the form of a user-friendly, menu-driven structure.Research financially supported by Dean's Professorship, College of Business, the Ohio State University.     

基于经验数据评价的非参数系统分析方法

针对以往权重确定型评价方法中存在权重确定困难、忽视指标个性差异等弱点,以及传统数据包络分析方法难于评价非效率问题,给出了一种基于样本评价决策单元整体绩效的非参数方法,构造了相应的数学模型,并对模型的含义、模型性质以及模型的求解方法进行了分析.同时探讨了该方法在决策单元的有效性度量与排序、决策单元的无效原因分析中的应用.最后,应用该方法分析了中国西部地区工业企业经济效益状况.