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

Madjid Tavana, Mehdi Toloo, Nazila Aghayi, Aliasghar Arabmaldar

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number114117
JournalExpert Systems with Applications
Publication statusPublished - 1 Apr 2021


  • Cross-efficiency evaluation
  • Data envelopment analysis
  • Interval approach
  • Robust optimization
  • Uncertain data
  • Undesirable outputs


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