A new robust optimization approach to most efficient formulation in DEA

Subject Areas : Data Envelopment Analysis

Reza  Akhlaghi ¹ (Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mohsen  Rostamy-Malkhalifeh ² (Department of Mathematics, science and research Branch, Islamic Azad University, Tehran, Iran)
Alireza  Amirteimoori ³ (Applied Mathematics Department, Rasht Branch, Islamic Azad University, Rasht, Iran)
Sohrab  Kordrostami ⁴ (Department of mathematics, Islamic azad University of Lahijan, Lahjan, Iran)

Received: 2023-12-16 Accepted : 2024-01-09 Published : 2023-06-01

Keywords: Uncertainty, Data Envelopment Analysis (DEA), Robust Optimization, Interval data, Optimistic Counterpart,

Abstract :

In this article, we investigate a new continuous linear model with constraints for the direct selection of the most efficient unit in the analysis of data coverage presented by Akhlaghi et al. (2021) on uncertainty robust optimization. Considering the importance of incorporating uncertainty into performance evaluation models in the real world and its increasing application in various problems, we propose a robust optimization approach. Given the discrete and non-convex nature of the introduced models for selecting the most efficient decision-making unit, examining the dual and finding an optimistic scenario is practically impossible. Therefore, by utilizing the linear model presented by Akhlaghi et al. (2021) with constraints for identifying the most efficient unit, we can investigate the robustness of the desired model using(BS )Bertsimas and Sim's (2004) robust estimation method while also considering uncertainty. We aim to demonstrate that employing a robust formulation leads to reliable performance in uncertain conditions