Data envelopment analysis (DEA) is a model for measuring the efficiency of decision-making units (DMUs). The majority of DEA models suffer from drawbacks, in particular, changes in the weights of inputs and outputs. Consequently, the efficiency of DMUs is measured with different weights and so it is important to establish how to evaluate all DMUs using a common weight to optimize their efficiency at the same time. This study provides a new algorithm to overcome the weaknesses of the previous model. The proposed algorithm based on the bootstrap simulation establishes a bound for the input and output weights. Common weights are obtained by solving this model using bounded weights. According to the results of a numerical example solved by this model, it outperforms conventional models in terms of ranking DMUs.
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Amiri, A., Saati, S. & Amirteimoori, A. Determining a common set of weights in data envelopment analysis by bootstrap. Math Sci (2020). https://doi.org/10.1007/s40096-020-00344-7
- Common weights
- Data envelopment analysis