improving discrimination power based on reducing dispersion of weights in data envelopment analysis

The main drawbacks that arise for data envelopment analysis (dea) are: lack of discriminationpower amongst efficient decision making units (dmus) and scattering input-output weights. in thedea, sometimes the mismatch of the input or output weights in the decision-making units (dmus)under consideration leads to assigning higher weight to variables with the less significance and/or thelower or zero weight to the variables with high significance. accordingly, most dea models introducemore than one efficient dmu in evaluating the relative efficiency of decision-making units. the presentpaper is conducted to overcome these inabilities. in this trends, we present a novel dea model basedon minimizing the sum of absolute deviations of all input-output weights from each other. the proposedmodel provides to enhance the discrimination power and adjusts the balance dispersion of input-outputweights. finally, well-known numerical experiments are considered to demonstrate the efficiency andvalidation of the suggested model.