A Goal Programming Approach to Weight Dispersion in Data Envelopment Analysis

Abstract

In this study, a multi-criteria data envelopment analysis (MCDEA) model, used in the literature to moderate the homogeneity of weights dispersion, is solved using pre-emptive goal programming. The MCDEA model solved using pre-emptive goal programming gives the same relative efficiency as the classical DEA model while it improves the homogeneity of input-output weights. This conclusion is confirmed by the computational results obtained when the two models are applied to a real data set relative to the socio-economic performances of European countries and to randomly generated instances with various numbers of decision making units, inputs and outputs.