A heuristic method for choosing 'virtual best' DMUs to enhance the discrimination power of the augmented DEA model

Despite its intrinsic advantages and features that help elevate the discrimination power of the basic DEA (Data Envelopment Analysis) model, augmented DEA has two main drawbacks including unrealistic eciency scores and a great distance between its eciency scores and those obtained by the primary model. In this respect, this paper extends a heuristic method for dealing with both issues and improving the power of the augmented DEA model in performance evaluation. Since dierent virtual Decision Making Units (DMUs) yield various ranking results, the hierarchical clustering algorithm is applied, in this study, to select the best virtual DMUs to reduce the possibility of inappropriate eciency scores. Finally, to demonstrate the superiority of the proposed approach over previous approaches in the literature, two numerical examples are provided.