A slacks-based measure of super-efficiency in DEA with interval data

A generalized model for data envelopment analysis with interval data

Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.

In Data envelopment analysis (DEA), the best performers are called DEA efficient and the efficiency score of a DEA efficient unit is denoted by unity. Ranking DEA efficient units is an interesting subject. This paper proposed a super-efficiency DEA model based on slack-based measure to evaluate engineering camouflage projects. A slack-based measure of DEA model was introduced, and then it was extended to super-efficiency issue. The method differs from the traditional radial measure, it takes account of the slacks of decision making units (DMU). The efficient DMUs can also be ranked completely. A numerical example illustrates the availability in the evaluating process of engineering camouflage projects.

A non-parametric linear programming-based data envelopment analysis (DEA) has been broadly applied in many sectors to measure the performance of decision-making units (DMUs). Basic DEA models such as CCR and BCC can bifurcate the DMUs into efficient and inefficient sets but cannot further identify the most efficient DMUs. Numerous methods have been proposed to address this issue and allotted ranks to DMUs. The main objective of the current paper is to implement an integrated DEA with Shannon's entropy to measure efficiencies and to rank hotels and restaurants (H&R) operating in India. The present study is carried out by integrating an orientation independent CCR, orientation dependent BCC, and non-oriented SBM with Shannon's entropy. The combined DEA-Shannon's entropy method provides comprehensive efficiency scores (CES) to all DMUs. The adopted methods have been applied to the selected 45 H&R companies in India for the year 2019-2020.

Greenhouse gas (GHG) is a main cause for climate anomalies, and thus, declining GHG emissions becomes an essential task for enterprises, especially for international liner shipping companies, to achieve the goal of corporate social responsibility (CSR). To liner shipping companies, main emitting gases are CO2, SOx, and NOx that cause marine pollution and global warming. To decline environmental warming and marine pollution, efficiency measure about GHG emissions for liner shipping companies is important. Due to data specifications of GHG, slack-based measure (SBM) data envelopment analysis (DEA) and super SBM DEA models focus on undesirable outputs are proposed and integrated into new DEA models. Through these models above, GHG emissions of liner shipping companies for varied years viewed as peer decision-making units (DMUs) are measured. Then, relative efficient DMUs are useful references for GHG emissions of liner shipping companies in emitting policies and technologies to reduce marine pollution.

DEA model with shared resources and efficiency decomposition

In this paper, we analyze the eco-efficiency by using the data envelopment analysis (DEA) method. Classical radial DEA model requires equal proportional reduction in each environmental pressure, and does no take slacks into account the evaluation of efficiency. In order to assess eco-efficiency more accurately, a modified radial DEA model is proposed, in which different environmental impacts are decreased to different proportions. Then, its properties are discussed. Based on the model, a modified radial DEA model with unequal weights is put forward. Finally, we use a real data set of Hubei province from 1995 to 2012, to illustrate the proposed models and properties. The results indicate the year is eco-effective for the modified radial DEA model if and only if the year is eco-effective to classical radial DEA model. And eco-efficiency of the modified radial DEA model is not greater than that of the classical radial DEA model. Moreover, the Pearson correlation exhibited between the two kinds of models is statistically significant. The weights of proportions have little impact on the rank of decision making units.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

Survey on rank preservation and rank reversal in data envelopment analysis

In most models of data envelopment analysis (DEA), the best performers have the efficiency score of unity. Usually there are plural decision making units (DMUs) which have this ‘efficient status’ and DEA cannot provide more information about these efficient DMUs. Discrimination between these efficient DMUs is an interesting research subject. In effect, most conventional methods to rank efficient DMUs are based on the concept of common weights analysis (CWA). In this paper, after reviewing the existing common weights models, we propose a new methodology to rank the common weights models for the performance indices of only DEA efficient DMUs based on a voting model. Also, an approach for combining the results obtained from the common weights models is presented. Then, we give an example to illustrate our approach, and finally the new method is employed to rank efficient units in a real world problem.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

In this paper, we have formulated Data Envelopment Analysis (DEA) models to reduce the inputs in an inefficient Decision Making Unit (DMU) when the specific inputs are under the constant sum constraint. We have also extended the models to reallocate the excess input without any reduction in efficiency of other DMUs. These DEA models and methods developed in this work will help decision makers in developing an optimal strategy to transfer excess input to other DMUs. Theoretical results have been illustrated with the help of a case study.

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.

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.