Open Access Research Article

Effect of Returns to Scale in Different DEA Models on Evaluating Efficiency by Considering Uncertainty in Data: Application from Hospitals

Authors

Aref Shayan * Department of Industrial Engineering, Islamic Azad University, Saveh Science and Research Branch, Iran.
Seyyed Esmaeel Najafi Department of Industrial Engineering, Islamic Azad University, Science and Research Branch,Tehran, Iran.
Mahnaz Ahadzadeh Namin Department of Mathematics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/ahse.v1i1.30

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Abstract

One of the most essential methods for assessing the efficiency of Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). This method is nonparametric, and one of the most critical issues is considering uncertain data in evaluating and ranking DMUs. Robust Data Envelopment Analysis (RDEA) is the approach for measuring the relative efficiency of DMUs by considering uncertain data. In this paper, we developed a RDEA on the Variable Returns to Scale (VRS) approaches and compared the results of RDEA based on the BCC model with RDEA based on the CCR model of DEA. By using Robust optimization, we wrote the RDEA. For the Robust optimization, two approaches are introduced: One is the Ben-Tal and Nemirovski approach, and the other is the Bertsimas et al. approach. In this paper, we used the Bertsimas et al. approach because this approach, unlike the Ben-Tal and Nemirovski approach, is a linear programming problem and is not hard to solve.

Keywords

Data envelopment analysis Robust optimization Robust data envelopment analysis Constant returns to scale Variable returns to scale

References (16)

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Published 2024-03-11
Issue Vol. 1 No. 1 (2024): Annals of Healthcare Systems Engineering
Section Articles
Document Type Research Article
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Shayan, A. ., Najafi, S. E. ., & Ahadzadeh Namin, M. . (2024). Effect of Returns to Scale in Different DEA Models on Evaluating Efficiency by Considering Uncertainty in Data: Application from Hospitals. Annals of Healthcare Systems Engineering, 1(1), 51-60. https://doi.org/10.22105/ahse.v1i1.30

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