A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today’s performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model’s input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.     

A target-oriented DEA model for regional construction productive efficiency improvement in China

China’s construction industry is a major contributor to economic growth during the past decade, but the industry has been mainly investment-driven without significant productivity improvement yet with high disparity across different regions. In the context of unbalanced regional development and resource allocation, stagnant performance of construction productivity is a common concern hindering the rapid and sustainable development of regional construction industry. The long-standing and escalating regional differences would not only undermine the overall performance of construction productivity, also lead to inefficient resource allocation and inappropriate target settings for future development. To address these issues, this study developed an overall efficiency measurement model for the construction industry, and then applied a Target-Oriented (TO) Data Envelopment Analysis (DEA) model to identify the optimal pathways for low efficiency provinces with the achievement of stage-wise targets in different regions of China. Comparison was made on construction productive efficiency, resource utilization, and recommendations were made for further improvement in three regions with different levels of economic development. The analytical and empirical results can provide insightful information for policy making and strategic planning with progressive efficiency improvement of regional construction industry of China.     

How the environment determines banking efficiency: a comparison of banking firms in Taiwan,Hong Kong,and Mainland China

This study uses the three‐stage data envelopment analysis (DEA) model to explore the true managerial efficiency of the banking firms in Taiwan, Hong Kong, and Mainland China. The empirical results indicate that the environmental conditions have a significant impact on banking efficiency. When the country‐specific situations are important factors in explaining efficiency difference, the common frontier estimates obtained by neglecting those factors can generate biased and overestimated inefficiency levels. With findings obtained from the slack variable analyses, the current study can provide inefficient banks with ways to reduce their input waste through the adjustment of input allocations. The findings also confirm the importance of the three‐stage DEA and its applications in determining the true managerial efficiencies of banks. Without the three‐stage DEA, the management could be misguided when making strategic decisions and conducting inappropriate resource allocation.     

考虑环境处置性的DEA综合效率模型

由于传统的径向或非径向DEA（Data Envelopment Analysis）模型在追求投入或产出最大化改进时角度不同,并且模型中的污染物往往采用强可处置假设,考虑因此造成的评价偏离实际生产过程、区分力差等问题,提出环境弱处置性下的基于灰关联的DEA径向与非径向综合效率模型。基于环境DEA技术提出了一个径向与非径向特征结合的综合效率模型研究框架;基于投入变量序列的灰关联构造了接近度指标,从而获得框架中连接与测量径向与非径向模型的特征参数,将其置于框架中得到最终评价结果。以2010年中国29个省份综合效率评价为算例,结果验证了模型的有效性。     

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.     

Using the DEA-R model in the hospital industry to study the pseudo-inefficiency problem

Because of the unreasonable and unnecessary weight restriction assumption, a barely noticeable deficiency in pseudo-inefficiency occurs at times when applying a CCR model. The CCR was the first model designed for Data Envelopment Analysis (DEA); it remains its most popular model. To detect this type of unobvious deficiency, this study compared the following: the efficiency score and optimal weight set of an input oriented CCR model (CCR-I); the model with a weight restriction assumption; an input-oriented ratio-based DEA model (DEA-R-I); and the model without it. In this case study of hospitals, pseudo-inefficiency was discovered and the reason behind it determined. Further, this study proved that the DEA-R-I is a valid model. Because DEA-R-I is valid and without a weight restriction assumption, this study puts forward the DEA-R-I as a capable substitutive model for CCR-I in order to avoid pseudo-inefficiency.     

应用Matlab解决常用DEA模型的评价分析

阐述多种常用的DEA评价模型,利用矩阵形式表述模型结构,应用Matlab对这些模型的评价程序进行编写.使常用的DEA评价问题可以通过Matlab软件解决,有效地解决了DEA循环计算导致的计算量大和复杂的问题.编写的程序分别为传统DEA-CCR模型及其对偶模型、BCC模型、最劣CCR模型和超效率DEA,为以后的DEA评价问题和模型改进提供了便利.     

考虑中间变量产出时滞性的两阶段评价

传统DEA模型假设特定时期的产出是通过消耗同一时期的投入产生的,这个潜在的假设不适用于某些绩效评价,即一个特定时期的产出不仅需要消耗该时期的投入,而且需要消耗之前多个时期的投入。为了更加客观地评价两阶段过程的效率值,构建了考虑中间变量产出时滞性的两阶段DEA模型,同时将其与Malmquist生产率指数相结合,寻求时间序列上的动态变化,从而找到效率变化的原因。并将提出的方法应用到中国30个省份创新效率的评价中,结果表明,考虑中间变量产出时滞性的模型排名更客观;创新效率不仅与研发阶段和经济转化阶段的效率密切相关,而且还受外部因素的影响。     

基于共享回馈DEA模型的中国省际 高技术产业创新效率研究

数据包络分析(data envelopment analysis,DEA)已被证明是测度高技术产业创新绩效的好方法,但现有文献忽略了现实中企业会将创新带来的经济效益回馈至两个子阶段进行再研发和生产,从而保证持续创新.基于此,结合高技术产业创新过程的特征,将其分为技术研发和商业转化两个子阶段,提出考虑共享回馈DEA模型的两阶段效率测度模型,不仅拓展了数据包络分析方法,也促进了创新绩效管理研究.实证结果表明:中国高技术产业整体效率良好,仍有提升空间,但省际发展不均衡,各省内部不同阶段效率也存在明显的差异,实行有针对性的管理是提升绩效的有效措施.     

Spatial Pattern of Regional Urbanization Efficiency: An Empirical Study of Shanghai

China is the fastest urbanizing area in the world. The complex urban systems not only create more socioeconomic output (such as GDP), but also bring more infrastructural costs and environmental losses. Thus, urban research has concentrated on urbanization efficiency toward efficient and spatially coordinated instead of urban size. In this paper, we use data envelopment analysis (DEA) to estimate how well districts in Shanghai utilize their resources. Based on data for five inputs and three outputs, we applied an input-oriented BBC Model and an output-oriented CCR Model for Decision Making Units (DMUs) assessment and urbanization efficiency analysis. In addition, the spatial pattern of relative urbanization efficiency is examined by spatial autocorrelation model. The research result indicates that it is in downward trend mainly affected by technical constraints, 11 out of 17 districts in Shanghai are efficient, and the urbanization efficiency gap between Pudong New district and Huangpu district is significant. The districts’urbanization efficiency in south areas are higher than that in middle and northeast area.     

基于SBM模型的中国省际全要素能源效率和污染排放效率研究

本文使用处理非期望产出的SBM模型，利用2001．2010年省际面板数据计算中国30省份的能源效率和污染排放效率及影响因素。研究结果表明，中国整体的能源效率和排放效率均在低位运行，有下滑趋势，且排放效率表现相对低落。随着时间推移，各省份和全国的能源效率和排放效率差距也有扩大趋势。整个研究期间，北京、上海、广东、海南等四地的能源效率和排放效率值均为1，实现了资源节约、环境友好的双赢局面，其他地区在节能减排上仍存在程度不一的改善空间。Tobit模型的回归结果表明，第二产业比重越高，越不利于提高各省能源效率和排放效率；政府影响力对各省能源效率和排放效率的影响为负值；贸易依存度、人口密度对各省能源效率和排放效率有正向显著影响。最后，本文提出了有关政策建议。     

Using a hybrid two‐stage DEA model to analyze the profitability and marketability of medical manufacturing firms

The purpose of this study is to develop a modification of the model developed by Chen and Zhu in 2004. Calculating stage and overall efficiencies precisely and consistently has become a major challenge of the two‐stage DEA model. However, most other models do not calculate the optimality of intermediates. Although the model developed by Chen and Zhu measures the optimality of intermediates, the calculated efficiency scores still have some shortfalls. The modified model, named the hybrid two‐stage DEA model, fills the gap between calculating the optimality of intermediates and the consistency of overall efficiency scores. In addition to obtaining an accurate measurement for the optimality of intermediates, the model confines efficiency scores to a range from zero to one (a ratio efficiency score). In an empirical evaluation, we use data from 64 medical manufacturing firms to test the performance of the hybrid model and offer recommendations for the industry.     

Analysis of the environmental efficiency in China based on the DEA cross-efficiency approach under different policy objectives

The existing studies on environmental efficiency evaluation generally have the problem of efficiency overestimation. To solve this problem, a new data envelopment analysis (DEA) cross-efficiency approach with undesirable outputs is developed to evaluate environmental efficiency from the perspectives of both self-evaluation and peer evaluation. Then, three new evaluation strategies, namely, economic development strategy, environmental protection strategy, and win–win strategy, are proposed to reflect the needs of decision makers under different policy objectives. The proposed cross-efficiency approach with different evaluation strategies not only realizes the cross evaluation of environmental efficiency, but also guarantees the relative uniqueness of the optimal solution on the basis of the preferences of decision makers. Combining the metafrontier DEA approach and DEA window analysis, a new cross-efficiency analytical framework is constructed to gradually analyse the influences of policy objectives, technology heterogeneity, and dynamic correlation on the environmental efficiency. Subsequently, the environmental efficiency of China's economic development during 2006–2015 is in-depth analysed on the basis of the proposed analytical framework, and some interesting conclusions, and some useful suggestions are obtained.     

Performance Evaluation of Public Bus Transportation by Using DEA Models and Shannon’s Entropy: An Example From a Company in a Large City of China

The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis (DEA) model and Shannon’s entropy. This company operates 37 main routes on the backbone roads. Thus, it plays a significant role in public transportation in the city. According to bus industry norms, an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands. For passenger satisfaction, passenger waiting time and passenger-crowding degree are considered, and they are undesirable indicators. To describe such indicators, a super-efficient DEA model is constructed. With this model, by using actual data, efficiency is evaluated for each bus route. Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results. Also, sensitivity analysis is presented. Therefore, the results are meaningful for the company to improve its operations and management.      

Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis

Where different supply chain planning algorithms are used, generally similar results may pose some challenges on the differentiating powers of evaluating different production schedules because of the increasing complexity of a supply chain network structure. For the comparison purpose, performance evaluation of different supply chain planning algorithms aims to use different supply chains models with different demands, capacities, and commonality through efficiency perspective by using a modified network rational data envelopment analysis (DEA) model. The proposed DEA model has the abilities: (1) to treat only undesirable outputs that exist without normal output, and the situation where input and output are both zero by introducing two new parameters to denote the maximum inventory and amount of delayed demands of a given node in a given time period; and (2) to evaluate the effect of the undesirable outputs/inputs on efficiency with assumption that they leave the system at the end of the current time period and re-enter the system at the beginning of the next time period. To prove the effectiveness of this DEA model, eighteen scenarios with different demands, capacities, and multiple periods are compared. In addition, this study tests the DEA model on a wafer testing/probing operation of a leading global semiconductor manufacturing and testing company in Taiwan by internal supply chain perspective. Results show that the DEA model proposed in this study can be used to assess the efficiency of a real-world operation with undesirable outputs/inputs, such as inventory and delayed demands.     

单幅光学遥感影像去霾算法及评价综述

光学遥感影像经常受到云或霾影响,而在多数情况下极少能获取辅助数据用于遥感影像去霾;因此单幅光学遥感影像的图像处理去霾算法成为遥感影像预处理的重要技术。目前,不同研究者设计了多种算法,但是缺乏系统性的总结与对比分析,本文旨在系统性地总结单幅遥感影像去霾算法的研究进展,并提供典型算法的基本原理、优缺点及适用场景。采用文献综合分析方法从霾条件影像成像模型、基础原理和结果评价3方面对当前的去霾算法进行归类总结和原理剖析,然后结合具体应用场景分析算法的适用范围和存在问题,并提出可行的解决方案。常见的去霾算法大体可归纳为暗目标减法、滤波法、暗通道先验法和经验变换法4类,这些算法采用的霾条件影像成像模型包括加法模型、霾传输衰减模型和照明—反射模型等;在算法的评估中,常用的手段有主观分析方法、影像光谱特征分析方法以及图像质量指标评估法等。现有算法并不能适用于所有的场景或图像,存在模型参数难以自适应调整、模型对特殊地物类型敏感、处理结果失真严重等问题;算法的评价主要采用主观对比分析方法,根据应用需求构建客观指标成为目前的热点方向。     

基于DEA模型的区域成人教育资源配置效率研究

探讨中国成人教育资源配置效率对优化成人教育资源配置、调整未来成人教育改革方向具有重要意义.通过构建投入产出指标体系,运用数据包络分析(DEA)模型对我国30个省市区的成人教育资源配置效率进行评价分析.结果表明,我国省市区成人教育普遍存在教育资源配置失效的问题,其中教育资源配置失效的省市区大部分存在着严重的固定资产投资、土地使用等投入冗余现象,但基本不存在产出不足的情况.针对分析结果,成人教育主管部门应当密切关注各地区的教育发展差异,特别是教育资源配置效率差异,统筹兼顾, 推动我国成人教育资源的高效配置和成人教育事业的健康发展.     

Evaluation of mutual funds using multi-dimensional information

To make better use of mutual fund information for decision-making we propose a coned-context, data envelopment analysis (DEA) model with expected shortfall (ES) modeled under an asymmetric Laplace distribution in order to measure risk when evaluating performance of mutual funds. Unlike traditional models, this model not only measures the attractiveness of mutual funds relative to the performance of other funds, but also takes the decision makers’ preferences and expert knowledge/judgment into full consideration. The model avoids unsatisfying and impractical outcomes that sometimes occur with traditional measures and it also provides more management information for decision-making. Determining input and output variables is obviously very important in DEA evaluation. Using statistical tests and theoretical analysis, we demonstrate that ES under an asymmetric Laplace distribution is reliable and we therefore propose the model as a major risk measure for mutual funds. At the same time, we consider a fund’s performance over different time horizons (e.g., one, three and five years) in order to determine the persistence of fund performance. Using the coned-context DEA model with ES value under an asymmetric Laplace distribution, we also present the results of an empirical study of mutual funds in China, which provides significant insights into management of mutual funds. This analysis suggests that the coned context measure will help investors to select the best fund and fund managers in order to identify the funds with the most potential.     

Economic Efficiency Analysis of Wafer Fabrication

Economic efficiency analysis of semiconductor fabrication facilities (fabs) involves tradeoffs among cost, yield, and cycle time. Due to the disparate units involved, direct evaluation and comparison is difficult. This article employs data envelopment analysis (DEA) to determine relative efficiencies among fabs over time on the basis of empirical data, whereby cycle time performance is transformed into monetary value according to an estimated price decline rate. Two alternative DEA models are formulated to evaluate the influence of cycle time and other performance attributes. The results show that cycle time and yield follow increasing returns to scale, just as do cost and resource utilization. Statistical analyses are performed to investigate the DEA results, leading to specific improvement directions and opportunities for relatively inefficient fabs. Note to Practitioners-Speed of manufacturing is an important metric of factory performance, yet it has long been a challenge to integrate its value into overall performance evaluation. However, for many semiconductor products, a predictable rate of decline in selling prices makes it possible to transform time value into monetary value. This study employs a novel method to incorporate a speed metric into economic efficiency evaluation and thereby provide a guideline for improving fab efficiency in manufacturing practice. Furthermore, this study integrates factory productivity and cycle time into a relative efficiency analysis model that jointly evaluates the impact of these two factors in manufacturing performance. In particular, we validate this approach with data from ten leading wafer fabs obtained by the Competitive Semiconductor Manufacturing Program and we discuss managerial implications.     

An improvement in DEA cross‐efficiency aggregation based on the Shannon entropy

This paper improves a recently proposed Data Envelopment Analysis (DEA) cross‐efficiency aggregation method based on the Shannon entropy. The weights for determining cross‐efficiency are derived from minimizing the square distance of weighted cross‐efficiency and weighted CCR efficiency. Our calculation example indicates that this method may produce inappropriate weights, which is significantly inconsistent with a widely accepted viewpoint. A variance coefficient method based on the Shannon entropy is presented to overcome the drawbacks of the DEA cross‐efficiency aggregation method. In this study, comparisons of weights and cross‐efficiency scores are provided.