Volume 23, Issue 3 (9-2026)                   jor 2026, 23(3): 0-0 | Back to browse issues page

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Dormishi A, Hosseinzadeh Lotfi F, Rahmani Parchikolaei B, Najafi E, Azizi A. Introducing a Metaheuristic Algorithm for Solving Large-Scale Problems in Data Envelopment Analysis. jor 2026; 23 (3)
URL: http://jamlu.lahijan.iau.ir/article-1-2106-en.html
Introducing a Metaheuristic Algorithm for Solving Large-Scale Problems in Data Envelopment Analysis
A. Dormishi , F. Hosseinzadeh Lotfi * , B. Rahmani Parchikolaei , E. Najafi , A. Azizi
Department of Mathematics, SR.C., Islamic Azad University, Tehran, Iran , farhad@hosseinzadeh.ir
Abstract:   (67 Views)
Data Envelopment Analysis (DEA) is one of the most widely used methods for evaluating the relative efficiency of Decision Making Units (DMUs). In conventional DEA, the optimization model must be solved separately for each DMU, resulting in a significant increase in computational time as the number of units grows. Furthermore, the limitations of linear programming solvers and the accumulation of numerical errors reduce the applicability of exact approaches for large-scale problems. This paper proposes a novel deterministic population-based metaheuristic algorithm to efficiently estimate the efficiency scores of large-scale DEA problems. The proposed approach incorporates an increasing gradient strategy to generate high-quality initial solutions close to the optimal region, thereby accelerating convergence and improving solution quality. Computational experiments conducted on randomly generated datasets and a real-world case study demonstrate that the proposed algorithm provides accurate and stable efficiency estimates within a reasonable computational time, making it suitable for large-scale DEA applications.  
 
Keywords: Data Envelopment Analysis (DEA), Metaheuristic Algorithm, Large-Scale Optimization, Decision Making Units, Efficiency Evaluation, BCC Model.
     
Type of Study: Research | Subject: Special
Received: 2021/12/13 | Accepted: 2022/07/5 | Published: 2026/09/11
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