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

Subject Areas : Mathematical Optimization

Yousef Badraghi ¹ , Shokrollah Ziari ² , Naghi Shoja ³ , Amir Gholam Abri ⁴

1 - Department of Industrial Engineering, Rudehen branch, Islamic Azad University, Rudehen, Iran
2 - Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran
3 - Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran
4 - Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran

Received: 2021-08-18 Accepted : 2023-01-02 Published : 2022-06-01

Keywords: Data envelopment analysis, Discrimination power, Dispersion of weights, Scale transformation,

Abstract :

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.

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