CCR Model Of Data Envelopment Analysis Research Articles

An extended aggregated ratio analysis in DEA

Data Envelopment Analysis (DEA) and Ratio Analysis (RA) are two widely used methods for measuring units’ productivity and any other criteria that could be assessed based on the available input and output variables. A number of researchers have studied DEA and RA and noted the positive and negative differences between them. Aggregated ratio analysis (ARA) model, which provide an important linkage between DEA and RA theory, is equivalent to the CCR DEA model, and this equivalence property offers a great deal of opportunities for DEA to be interpreted and applied in different ways. This paper extends the results of ARA model and proposes an extended aggregated ratio analysis (EARA) model, similar as the development from CCR model to BCC model in DEA context. The proposed model can offer an insight into the characteristic of returns to scale, playing the corresponding role as BCC model does. The numerical example is revisited in the paper and the results are compared.

DEA advanced models for geometric evaluation of used lathes

This paper presents a preliminary study of the quality of lathes using Data Envelopment Analysis (DEA) as a multicriteria tool. Its purpose is to aggregate several measurements that show geometrical errors to obtain one single measurement that will be free of subjective opinions. Due to the small number of DMUs, additional techniques other than the DEA CCR model (super efficiency, cross-evaluation, inverted frontier and weights restrictions) are reviewed and used to differentiate between otherwise identical efficiency scores. The specificity of this problem required some changes in the above-mentioned methods. The models were applied to four lathes at the Fluminense Federal University Machine Shop.

A comparative analysis of the DEA-CCR model and the VIKOR method

Data Envelopment Analysis (DEA) introduces a model for weights determination maximizing efficiency of the decision-making units. The primary focus of the DEA model is to compare decision-making units (alternatives) in terms of their efficiency in converting inputs into outputs. The multicriteria decision making (MCDM) method VIKOR uses a common set of weights expressing a decision maker's preferences. In contrast, the CCR model of DEA does not provide a common set of weights that could express the preferences of a decision maker. The weights in MCDM do not have a clear economic significance, but their use provides the opportunity to model the real aspects of decision making, such as the preference structure. A comparison of DEA and MCDM shows that DEA resembles MCDM, but the results differ. In spite of these differences, DEA could be used as a supplement for screening alternatives within MCDM. An application of DEA and MCDM is illustrated by an example of hydropower system planning.

An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development

In this paper, we use data envelopment analysis (DEA) to estimate how well regions in Serbia utilize their resources. Based on data for four inputs and four outputs we applied an output-oriented CCR DEA model and it appears that 17 out of 30 regions are efficient. For each inefficient unit, DEA identifies the sources and level of inefficiency for each input and output. An output-oriented set of targets is determined for 13 inefficient regions. In addition, the possibilities of combining DEA and linear discriminant analysis (LDA) in evaluating performance are explored. The efficient regions are ranked using a cross efficiency matrix and an output-oriented version of Andersen–Petersen’s DEA model and the results are analyzed and compared.

Efficiency bounds in Data Envelopment Analysis

This paper considers a proposal, by Chang and Guh, that the non-archimedean infinitesimal in the CCR data envelopment analysis model be replaced with a data-dependent finite magnitude. Whilst the intention of the proposal is clear: to reduce the CCR efficiency rating of certain problematic DMUs, it is found not to work in practice. An alternative implementation, which puts the CCR model into a mixed-binary linear programming framework, is developed. Chang and Guh's proposal is also related to the earlier modification to the CCR model, Constrained Facet Analysis. Both are seen as providing bounds on the relative efficiency of DMUs which are not properly enveloped in the CCR model.