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:: Volume 13, Issue 2 (3-2025) ::
2025, 13(2): 69-79 Back to browse issues page
A novel mixed-integer linear programming model for identifying the most efficient DMU in data envelopment analysis
M. Abbasi , A. Ghomashi , S. Shahghobadi
Department of Mathematics‎, ‎ Kermanshah Branch, Islamic Azad‎ ‎University, ‎Kermanshah, ‎Iran
Abstract:   (22 Views)
Finding the most efficient decision-making unit (DMU) in Data Envelopment Analysis (DEA) can provide deeper insights into the performance of efficient units. Over the years, several methods have been proposed to improve the ability of DEA models to distinguish between DMUs, often by aiming for stronger ranking capabilities. In this study, we present an enhanced model based on mixed-integer linear programming (MILP) to identify the most efficient DMU. The model is designed so that only one DMU can achieve an efficiency score equal to one, while all others receive scores strictly less than one. This structure enhances the model’s ability to fully rank all units, while using fewer constraints compared to traditional full-ranking models. To demonstrate its effectiveness and compare it with two well-known models, the proposed model is applied to two real-world examples from the literature. These findings show that proposed model clearly outperforms of the reviewed models—not just in theory, but in practice too.
 
Keywords: Data Envelopment Analysis, Most Efficient DMU, Mixed Integer Linear Programming, Ranking.
Full-Text [PDF 414 kb]   (10 Downloads)    
Type of Study: Research | Subject: General
Received: 2025/01/15 | Accepted: 2025/03/16 | Published: 2025/04/1
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Abbasi M, Ghomashi A, Shahghobadi S. A novel mixed-integer linear programming model for identifying the most efficient DMU in data envelopment analysis. International Journal of Applied Operational Research 2025; 13 (2) :69-79
URL: http://ijorlu.liau.ac.ir/article-1-703-en.html


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Volume 13, Issue 2 (3-2025) Back to browse issues page
ژورنال بین المللی پژوهش عملیاتی International Journal of Applied Operational Research - An Open Access Journal
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