943. Enhancing Robust Efficiency Assessment in Data Envelopment Analysis Under Uncertainty
Invited abstract in session TB-60: DEA under uncertainty, stream Data Envelopment Analysis and its applications.
Tuesday, 10:30-12:00Room: Western LT
Authors (first author is the speaker)
| 1. | Aliasghar Arabmaldar
|
| Business Analytics and Systems, University of Hertfordshire |
Abstract
This paper introduces two novel robust data envelopment analysis (DEA) models to enhance efficiency assessment under uncertainty. The first model employs a correlated budgeted uncertainty set, capturing dependencies among uncertain parameters for more realistic evaluations. The second model leverages order statistics uncertainty, improving robustness by considering worst-case scenarios based on ranked data. A theoretical analysis establishes the properties and advantages of these models, demonstrating their effectiveness in addressing different uncertainty structures. To validate their performance, a comparative example benchmarks the proposed models against the conventional budgeted uncertainty set approach. The results highlight their strengths in providing more reliable and adaptable efficiency assessments. By integrating correlated dependencies and ranked uncertainty considerations, the proposed models advance robust DEA methodologies, offering valuable insights for decision-makers in uncertain environments.
Keywords
- Data Envelopment Analysis
- Robust Optimization
- Efficiency Analysis
Status: accepted
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