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.
Original language | English |
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Article number | 114117 |
Journal | Expert Systems with Applications |
Volume | 167 |
DOIs | |
Publication status | Published - Apr 1 2021 |
Keywords
- Cross-efficiency evaluation
- Data envelopment analysis
- Interval approach
- Robust optimization
- Uncertain data
- Undesirable outputs
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence