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The conventional paradigm in data envelopment analysis (DEA) is to develop an efficiency measurement model that assumes the input and output data are precise and equal to some nominal values. However, this paradigm does not take into consideration the inherent uncertainties in real-life performance measurement problems. As a result of these uncertainties, the input and output data may take non-nominal values and violate the basic assumptions in DEA. This phenomenon has motivated us to design a DEA model that is 'robust' and immune to uncertain data. We present a robust DEA model with a common set of weights (CSWs) under varying degrees of conservatism and data uncertainty. We use goal programming (GP) and compute the relative efficiencies of the decision making units (DMUs) by producing CSWs in one run. The proposed model uses a confidence criterion to produce a ranking of the DMUs and determine a set of efficient DMUs. We present a numerical example and a case study to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a performance measurement problem in the banking industry. [

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