عنوان مقاله [English]
Data envelopment analysis (DEA) is an approach for measuring the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and multiple outputs using mathematical programming. In conventional DEA models, a performance measure whether as an input or output usually has to be known. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Such variables are called flexible measures. This paper introduces a new “double-frontier DEA” approach for classification of flexible measures. In the proposed approach, each flexible measure is classified as either input or output, so that the efficiency of the DMU under evaluation is maximized. Therefore, classification of flexible measures using the proposed DEA approach is simpler and more logical. An example in UK higher education institution shows applicability of the proposed approach.
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