Uncertain random data envelopment analysis for technical efficiency

Abstract

Data envelopment analysis (DEA) is a classical and prevailing tool for estimating relative efficiencies of multiple decision making units (DMUs). However, sometimes DMUs’ inputs and outputs cannot be observed accurately in practical cases, and hence this paper attempts to propose an uncertain random DEA model to evaluate the efficiencies of DMUs with uncertain random inputs and outputs. The sensitivity and stability of this new model are further analyzed with the aim to figure out the stability radius of each DMU. Finally, a numerical example is presented for illustrating the proposed uncertain random DEA model.