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Performance assessment under negative data and uncertainty: The robust data envelopment analysis approach | ||
| Journal of Mathematical Modeling | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 03 شهریور 1405 اصل مقاله (508.91 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2026.33359.3047 | ||
| نویسندگان | ||
| Pejman Peykani* 1؛ Seyed Ehsan Shojaie2؛ Seyed Jafar Sadjadi2؛ Zahra Abbasi3؛ Cristina Tanasescu4 | ||
| 1Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran | ||
| 2School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| 3Department of Mathematics, University of Qom, Qom, Iran | ||
| 4Faculty of Economic Sciences, Lucian Blaga University of Sibiu, Sibiu, Romania | ||
| چکیده | ||
| This research introduces a novel robust framework grounded in Data Envelopment Analysis (DEA) for performance assessment and ranking of Decision-Making Units (DMUs) in the presence of negative data and deep uncertainty. From a methodological standpoint, the principal contribution is the derivation, via robust-optimization duality, of tractable linear robust counterparts of the Range Directional Measure (RDM) and Variant of Radial Measure (VRM) models under a composite Box-Polyhedral (budgeted) uncertainty set, requiring only interval information on the input/output data rather than any assumption on their probability distribution. The proposed model integrates the analytical strengths of the RDM and VRM models, which inherently accommodate negative data, with the principles of Robust Optimization (RO). This hybrid approach constructs robust efficiency scores by immunizing the assessment against simultaneous perturbations of all inputs and outputs within prescribed uncertainty bounds. This study integrates robust optimization with the RDM and VRM models so as to jointly address negative data and deep uncertainty within a single DEA framework. The framework’s practical efficacy is validated through an empirical application to stock market analysis, where firms are ranked using a set of financial indicators, including those that can assume negative values such as net profit or return on assets. The results confirm that the proposed Robust Data Envelopment Analysis (RDEA) approach effectively balances robustness with discriminatory power, offering a reliable decision-support tool for financial analysis and other domains characterized by complex and uncertain data environments. | ||
| کلیدواژهها | ||
| Data envelopment analysis؛ directional distance function؛ negative data؛ range directional measure؛ variant of radial measure؛ deep uncertainty؛ robust optimization؛ stock market | ||
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آمار تعداد مشاهده مقاله: 4 تعداد دریافت فایل اصل مقاله: 2 |
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