An approach to rank efficient DMUs in DEA based on combining Manhattan and infinity norms
محورهای موضوعی : Data Envelopment Analysis
Shokrollah Ziari
1
,
Manaf Sharifzadeh
2
1 - Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh,Iran
2 - Department of Computer, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran
کلید واژه: Data Envelopment Analysis (DEA), Ranking, Efficiency, Extreme efficient,
چکیده مقاله :
In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing interest in improving discrimination power in DEA yet. The aim of this paper is ranking extreme efficient DMUs in DEA based on exploiting the leave-one out idea and combining of Manhattan and infinity norms with constant and variable returns to scale. The proposed method has been able to overcome the existing difficulties in some ranking methods.
In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing interest in improving discrimination power in DEA yet. The aim of this paper is ranking extreme efficient DMUs in DEA based on exploiting the leave-one out idea and combining of Manhattan and infinity norms with constant and variable returns to scale. The proposed method has been able to overcome the existing difficulties in some ranking methods.
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