Dual-Role Factors for Imprecise Data Envelopment Analysis

In conventional data envelopment analysis (DEA), the observed inputs, outputs and dual-factors are assumed to be precise. However, we often observe imprecise and ambiguous data in practice. In this paper, we present an imprecise DEA model in the presence of dual-role factors to deal with the imprecise data. The resulting models are the mixed binary integer programming models that supply the best possible relative efficiencies from the optimistic and pessimistic viewpoints. After some theoretical discussions, the proposed models are illustrated with a numerical example.