Title

A robust cross-efficiency data envelopment analysis model with undesirable outputs

Document Type

Article

Publication Date

4-1-2021

DOI

10.1016/j.eswa.2020.114117

Abstract

Degenerate optimal weights and uncertain data are two challenging problems in conventional data envelopment analysis (DEA). Cross-efficiency and robust optimization are commonly used to handle such problems. We develop two DEA adaptations to rank decision-making units (DMUs) characterized by uncertain data and undesirable outputs. The first adaptation is an interval approach, where we propose lower- and upper-bounds for the efficiency scores and apply a robust cross-efficiency model to avoid problems of non-unique optimal weights and uncertain data. We initially use the proposed interval approach and categorize DMUs into fully efficient, efficient, and inefficient groups. The second adaptation is a robust approach, where we rank the DMUs, with a measure of cross-efficiency that extends the traditional classification of efficient and inefficient units. Results show that we can obtain higher discriminatory power and higher-ranking stability compared with the interval models. We present an example from the literature and a real-world application in the banking industry to demonstrate this capability.

Language

English

Comments

Tavana, M., Toloo, M., Aghayi, N. and Arabmaldar, A. (2021) ‘A Robust Cross-Efficiency Data Envelopment Analysis Model with Undesirable Outputs,’ Expert Systems with Applications, Vol. 167, Article 114117.

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