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The cross-efficiency evaluation (CEE) method, which was developed as a contribution to classical Data Envelopment Analysis (DEA), has been successively used to solve problems involving unrealistic weight distribution as well as problems that do not require prior information for ranking the decision making units (DMUs). Originally, the CEE method included the efficiency evaluations that were obtained for a DMU by the classical DEA for the reuse of optimal weights in the other DMUs. As the optimal weights in the classical DEA solutions usually have multiple solutions, this reduces the usefulness of the CEE method. Hence, this study suggests a new technique that could be used in the second stage of the CEE method for removing the problem of multiple optimal weights and for determining the reasonable ranks of DMUs. The performance of the proposed model is examined on real data set relative to the efficiencies of Turkey cities. [ABSTRACT FROM AUTHOR]

Abstract: Cross efficiency evaluation has long been proposed as an alternative method for ranking the decision making units (DMUs) in data envelopment analysis (DEA). This study proposes goal programming models that could be used in the second stage of the cross evaluation. Proposed goal programming models have different efficiency concepts as classical DEA, minmax and minsum efficiency criteria. Numerical examples are provided to illustrate the applications of the proposed goal programming cross efficiency models. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]