Modified Goal Programming Approach for Improving the Discrimination Power and Weights Dispersion

Abstract

Data envelopment analysis (DEA) is a technique based on linear programming (LP) to measure the relative efficiency of homogeneous units by considering inputs and outputs. The lack of discrimination among efficient decision making units (DMUs) and unrealistic input-outputs weights have been known as the drawback of DEA. In this paper the new scheme based on a goal programming data envelopment analysis (GPDEA) are developed to moderate the homogeneity and reasonability of weights distribution by using of facet analysis On GPDEA (GPDEA-CCR and GPDEA-BCC) models. These modifications are done by considering the lower bounds for each individual inputs and outputs weights in standard CCR model and an upper bound just for free variable of standard BCC model. In the both of the cases the mentioned modification preserved the inputs and outputs weights from zero value. The modified GPDEA models also improve the discrimination power of DEA. The advantages of each modified GPDEA-CCR and GPDEA-BCC models are shown by some examples.