Adding and removing an attribute in a DEA model: theory and processing

We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a data envelopment analysis (DEA) model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/inefficient status of decision–making units (DMUs) is affected. Reducing computational efforts is important since DEA is known to be computationally intensive, especially in large–scale applications. We present a comprehensive theoretical study of the impact of attribute insertion and removal in DEA models, which includes sufficient conditions for identifying efficient DMUs when an attribute is added and inefficient DMUs when an attribute is removed. We also introduce a new procedure, HyperClimb, specially designed to quickly identify some of the new efficient DMUs, without involving LPs, when the model changes with the addition of an attribute. We report on results from computational tests designed to assess this procedure's effectiveness.