Data envelopment analysis (DEA) is an optimization-based methodology used for the assessment of efficiency of organizations. Traditional DEA models are based on the vectors of inputs (resources) and outputs (products or services) of the observed organizations. DEA models are often enhanced by additional value judgements stated as tradeoffs between inputs and outputs or as dual weight restrictions. In the paper “Proper and consistent production trade-offs in models of data envelopment analysis”, Podinovski and Papaioannou showed that such additional value judgments may be contradictory (not proper) and develop analytical and computational tests for their identification. Any value judgments that are not proper indicate an error in their assessment. The developed approach is equally applicable in multicriteria decision analysis in which the tradeoffs between criteria are specified imprecisely by a set of linear inequalities stated in terms of criterion weights. Not proper tradeoffs result in the weights of some criteria (identifiable by the developed computational approach) being equal to zero, which effectively excludes such criteria from the analysis.
Podinovski et al. (Mon,) studied this question.