Modely analýzy obalu dat s fixním součtem výstupů

PurposeThe purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.Design/methodology/approachThis study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.FindingsThe data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.Originality/valueBy adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

A traditional DEA (data envelopment analysis) model is useful for estimating the best performances for a successful and a more efficiency DMU (decision-making unit). This study uses a traditional DEA model to estimate the efficiency score of bank z in Taiwan and get a good efficiency score based on the 2006 data, however the bank z was taken over by the government in early 2007. In illustrating this paradox phenomenon, the study reconsiders the tradition DEA model by contributing a worst-practice frontier under a traditional DEA model. Findings: the bank z is located on the intersection of the worst-practice frontier and the traditional DEA model frontier. This study provides a critical determinant of a bank’s failure that a bank has a location such as the bank z.

The aim of this paper is to provide a multi-activity data envelopment analysis (DEA) model which can represent both quayside and landside operations in a unified framework. The model is applied to simultaneously estimate the overall container terminal efficiency: quayside operation efficiency and landside operation efficiency. To do this, 20 selected container terminals in China for the year 2006 are selected for empirical applications. The results show that the average quayside operation efficiency is higher than the average landside operation efficiency (0.630 vs. 0.588), but these measures differ significantly for individual terminals. The main advantage of the multi-activity DEA model is that it can be utilized to detect the source of poor performance of container terminals rather than just evaluating the overall efficiency as in the traditional DEA model.

DEA (Data Envelopment Analysis) is a nonparametric approach that has been used to estimate fuzzy portfolio efficiency. In this paper, we propose an approach under the fuzzy theory framework that can both improve the DEA frontier and suggest a replicable benchmark for investors. We first construct an improved DEA model using the proposed approach and then investigate the relationships among the evaluation model based on a portfolio frontier, the traditional DEA model and the improved DEA model. We show the convergence of the improved DEA model under the fuzzy framework. The simulation indicates that the improved DEA frontier is closer to the portfolio frontier than to the traditional DEA frontier. More importantly, we incorporate the diversification DEA model and improved DEA model to analyze the performance of China’s open-end fund. The empirical results indicate that the improved DEA model not only provides a quicker way to assess the investment funds compared to the diversification DEA model but also makes up for the shortcoming of the traditional DEA model, which overestimates the fuzzy portfolio efficiency.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

Purpose The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated. Design/methodology/approach DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances. Findings All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models. Originality/value As we are aware, this is the first survey in this topic.

Due to a lot of carbon dioxide (CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) emissions, global warming has cause extreme weather, which seriously affects the life of human beings, animals and plants. The CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions come mainly from the burning of fossil energy. Therefore, a novel economy and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions evaluation model based on the environmental data envelopment analysis cross model (DEACM) is proposed to analyze and optimize energy structures of some countries and areas in the world. The inputs of the proposed models are renewable energy, hydroelectricity, natural gas, oil and coal. And the desired output and undesired output are per capita gross domestic product (GDP) and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, respectively. The results show that compared with the traditional data envelopment analysis (DEA) model, the mutual evaluation system adopted by the DEACMs model fully considers the relationship among each DMUs, and the evaluation efficiency values are more comprehensive and practical than the traditional DEA model. The energy input structures in developed countries or areas are more reasonable, and the technology in energy efficiency consumption and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> control is more advanced than those of developing countries. In addition, according to the comparison of the benchmark countries, such as New Zealand and Iran, the energy consumption structures of other ineffective countries or areas can be adjusted more rationally and their energy efficiency values can be further improved.

The electric power industry is vital to the economic development of the country. On the basis of existing researches, this paper developed an electric power industry efficiency evaluation index system with ten input indicators and five output indicators and applied principal component analysis(PCA) method to simplify the index system in order to meet the requirements of super-efficiency data envelopment analysis(DEA) model. As a result, labor input, capital input, technology equipping rate, total output value and total profit were selected as the performance evaluation indicators for the electric power industry input and output index. Based on traditional DEA model, this paper established the super-efficiency DEA Model to evaluate the efficiency of power industry from both input and output perspectives. Finally, the power industry panel data of Jiangsu, China from 2005 to 2015 has been selected to evaluate the input and output efficiency of power industry in Jiangsu through the super-efficiency DEA Model.

Traditional data envelopment analysis (DEA) models ignore the influence of environmental variables and statistical noise, which may result in biased efficiency estimates. To solve this problem, three-stage DEA models have been proposed and widely applied in many areas. This study evaluates China’s regional energy efficiency by using a three-stage DEA model based on the statistical data of 2010 and discusses the divergence of three different efficiency assessment methods. The empirical results show that environmental factors indeed influence the regional energy efficiency performance. After adjusting the environmental variables, the national energy efficiency average estimated by the three-stage DEA model decreased significantly relative to the estimate of the traditional DEA model, but the environmental influence in different regions varies due to diverse features. Some regional averages were overestimated by using the traditional DEA model, while some regional averages were underestimated. The three-stage DEA model is able to reflect the true efficiency by eliminating environmental effects compared with other methods.

Airport efficiency is an important issue for each country. The classical DEA models use different input and output weights in each decision making unit (DMU) that seems not reasonable. We present a multiple objectives based Data Envelopment Analysis (DEA) model which can be used to improve discriminating power of DEA method and generate a more reasonable input and out weights. The traditional DEA model is first replaced by a multiple objective linear program (MOLP) that a set of Pareto optimal solutions is obtained by genetic algorithm. We then choose a set of common weights for inputs and outputs within the Pareto solutions. A gap analysis is included in this study that can help airports understand their gaps of performances to aspiration levels. For this new proposed model based on MOLP it is observed that the number of efficient DUMs is reduced, improving the discrimination power. Numerical example from real-world airport data is provided to show some advantages of our method over the previous methods.

Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case

China’s rapid economic growth is accompanied by increasing energy consumption and severe environmental problems. As sustainable development can only be achieved by reducing energy intensity, new energy and renewable energy investment, as well as improving traditional energy efficiency, is becoming increasingly important. However, past energy efficiency assessments using data envelopment analysis (DEA) models mostly focused on radial and non-radial DEA model analyses. However, traditional radial DEA models ignore non-radial slacks when evaluating efficiency values, and non-radial DEA models ignore the same proportionality as radial DEA when evaluating efficiency value slacks. To balance the radial and non-radial model characteristics and consider undesirable output, this study combines a modified Epsilou-based measure (EBM) DEA and undesirable output and proposes a modified undesirable EBM DEA model to analyze the efficiency of China’s new and traditional energy sources. The empirical results found that (1) most new energy investment in most municipalities/provinces rapidly grew from 2013 to 2016; (2) as the annual efficiency score was only 1 in Beijing, Inner Mongolia, Shanghai, and Tianjin, the other 26 municipalities/provinces need significant improvements; (3) traditional energy efficiency scores were higher than new energy efficiency; and (4) NO2 efficiencies are slightly better than CO2 and SO2 efficiencies.

In order to investigate the interrelationship between risk and efficiency, based on the multistage data envelopment analysis (DEA) model, this paper introduces a modified measure of risk-adjusted efficiency for the insurance industry in China from 1999 to 2006. By comparing the results from the traditional DEA model and multistage DEA model, it is evident that there are significant changes in the firms' efficiency scores and their related ranks. The results show that the efficiency scores of the insurance firms studied tend to be underestimated if the endogenous risk factors due to bad management are not considered. With the continued development of the insurance industry in China, the insurance firms' capacity to manage risk has shown to have a significant positive impact on the improvement of their efficiency. Further, the risk-adjusted efficiency advantage of non-state-owned enterprises over state-owned enterprises is not as significant as it is when using the unadjusted models. However, the efficiency gap between foreign and domestic insurance firms shows an increasing trend during the sample period.

Measuring Transport Systems Efficiency Under Uncertainty by Fuzzy Sets Theory Based Data Envelopment Analysis: Theoretical and Practical Comparison with Traditional DEA Model

The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model