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

Journal of Industrial Engineering International

دوره 18، شماره 2، شهریور 2022، صفحه 42-55   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/jiei.2023.1938148.1153

نویسندگان

Yousef Badraghi¹؛ Shokrollah Ziari^{* 2}؛ Naghi Shoja²؛ Amir Gholam Abri²

¹Department of Industrial Engineering, Rudehen branch, Islamic Azad University, Rudehen, Iran

²Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran

چکیده

The main drawbacks that arise for data envelopment analysis (DEA) are: lack of discrimination
power amongst efficient decision making units (DMUs) and scattering input-output weights. In the
DEA, 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 the
lower or zero weight to the variables with high significance. Accordingly, most DEA models introduce
more than one efficient DMU in evaluating the relative efficiency of decision-making units. The present
paper is conducted to overcome these inabilities. In this trends, we present a novel DEA model based
on minimizing the sum of absolute deviations of all input-output weights from each other. The proposed
model provides to enhance the discrimination power and adjusts the balance dispersion of input-output
weights. Finally, well-known numerical experiments are considered to demonstrate the efficiency and
validation of the suggested model.

کلیدواژه‌ها

Data Envelopment Analysis؛ Discrimination power؛ Dispersion of weights؛ Scale transformation

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