Data envelopment analysis (DEA) is a very popular mathematical programming technique that is used to evaluate relative efficiency of decision-making units (DMUs). Beside its popularity, DEA has some drawbacks such as unrealistic input-output weights. Therefore, even if they are important, inputs or outputs of some DMUs can be assigned zero (0) or very small weights. Furthermore, because of this drawback, weights can take extreme high values, which cause unrealistic results. Mecit and Alp  described the restricted model to handle these problems using additional constraints that include correlation coefficients for weight restriction. However, in the case that negative correlations occur between variables, the constraints related with these negative correlations become useless and have no contribution in the restriction of assurance region (AR), which may result in no contribution of additional constraints in the ARIII model. This study proposes a modification by taking into account absolute values of correlation coefficients to handle the negative correlation problem of ARIII for some special cases. (C) 2016 Elsevier Inc. All rights reserved.