Ranking Decision Making Units (DMUs) Using Support Vector Machines (SVM) & Ideal DMU

Abstract

Ranking of decision making units (DMUs) is an important issue in a production process. Therefore, it is one of the most frequently studied subjects in the theory and practice studies in Data Envelopment Analysis literature. Recently, machine learning-based methods have also been used for crutial problems in literature such as ranking of DMUs and determining the efficiency frontier. This study proposes a new hybrid approach to rank DMUs. This approach is based on the Support Vector Machines, which is a machine learning method, and the Ideal DMU, which has an important place in the DEA literature. The theoretical details of this method are explained, and the performance of the model is demonstrated through application and simulation studies.

Keywords

• Ranking
• Decision making unit
• Support Vector Machines
• Ideal DMU

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