Uncertain Range Directional Measure Model under Deep Uncertainty: A Robust Convex Programming Approach

Conventional data envelopment analysis (DEA) models cannot deal with negative and uncertain values. Accordingly, the main objective of current study is to present a novel robust data envelopment analysis (RDEA) approach that is capable to be used in the presence of negative values and uncertain data. Notably, to propose RDEA approach, range directional measure (RDM) model and robust convex programming approach are employed. Finally, the applicability and efficacy of the proposed robust range directional measure (RRDM) model is demonstrated by assessing the relative performance of 15 stocks from Tehran stock exchange. The results indicate on the efficacy of the presented RRDM model for performance measurement of DMUs in the presence of negative values and uncertainty environment.

Uncertain Range Directional Measure Model under Deep Uncertainty: A Robust Convex Programming Approach Keywords:

Data Envelopment Analysis , Robust Optimization , Negative Value , Uncertainty , Performance Measurement.