A new method based on the dispersion of weights in data envelopment analysis
One of the drawbacks of the data envelopment analysis (DEA) is the problem of lack of discrimination among efficient decision making units (DMUs) and hence yielding many numbers of DMUs as efficient. The main purpose of this study is to overcome this inability. In the case in which the minimization of the coefficient of variation (CV) for input-output weights is added to the DEA model, more reasonable and more homogeneous input-output weights are obtained. For this new proposed model based on the CV it is observed that the number of efficient DMUs is reduced, improving the discrimination power. When this new approach is applied to two well-known examples in the literature, and a real-world data of OECD countries, it has been seen that the new model yielded a more balanced dispersion of input-output weights and reduced the number of efficient DMUs. In addition, the applicability of the new model is tested by a simulation study. (C) 2007 Elsevier Ltd. All rights reserved.
data envelopment analysis coefficient of variation discrimination power weight dispersion weight restriction