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Pourmahmoud, Jafar, Abbasi, Ahad, Ghaffari-Hadigheh, Alireza. (1404). Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory. فصلنامه علمی زیست فناوری گیاهان زراعی, 10(2), 103-133. doi: 10.30473/coam.2025.73893.1293
Jafar Pourmahmoud; Ahad Abbasi; Alireza Ghaffari-Hadigheh. "Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory". فصلنامه علمی زیست فناوری گیاهان زراعی, 10, 2, 1404, 103-133. doi: 10.30473/coam.2025.73893.1293
Pourmahmoud, Jafar, Abbasi, Ahad, Ghaffari-Hadigheh, Alireza. (1404). 'Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory', فصلنامه علمی زیست فناوری گیاهان زراعی, 10(2), pp. 103-133. doi: 10.30473/coam.2025.73893.1293
Pourmahmoud, Jafar, Abbasi, Ahad, Ghaffari-Hadigheh, Alireza. Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory. فصلنامه علمی زیست فناوری گیاهان زراعی, 1404; 10(2): 103-133. doi: 10.30473/coam.2025.73893.1293

Network Data Envelopment Analysis and Uncertainty in Decision-Making‎: ‎A Three-Stage Model Based on Liu's Uncertainty Theory

Control and Optimization in Applied Mathematics
مقاله 4، دوره 10، شماره 2 - شماره پیاپی 20، مهر 2025، صفحه 103-133    اصل مقاله (645.58 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.73893.1293
نویسندگان
Jafar Pourmahmoud* ؛ Ahad Abbasi؛ Alireza Ghaffari-Hadigheh
Department of Applied Mathematics‎, ‎Azarbaijan Shahid Madani University‎, ‎Tabriz‎, ‎Iran.
چکیده
Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units‎. ‎In complex systems comprising multiple interconnected subsections‎, ‎Network DEA provides a structured framework for efficiency evaluation‎. ‎However‎, ‎traditional DEA models rely on the assumption of deterministic data‎, ‎which inadequately reflects the inherent uncertainty present in real-world scenarios‎. ‎Traditional uncertainty-handling methods‎, ‎such as fuzzy logic‎, ‎stochastic models‎, ‎and interval-based techniques‎, ‎often fail when there is limited historical data and when expert opinions significantly influence the dataset‎. ‎To address these limitations‎, ‎this study introduces an uncertain network DEA model based on Liu’s uncertainty theory‎, ‎facilitating a more accurate assessment of efficiency under conditions of data imprecision‎. ‎The proposed model is designed for three interconnected subsections and is further extended into a generalized multi-stage framework‎, ‎allowing it to adapt to increasingly complex systems‎. ‎Its effectiveness and practical applicability are demonstrated through two numerical case studies in the banking industry‎, ‎highlighting its capacity to support decision-making under uncertainty‎. ‎The findings emphasize the model's potential to enhance efficiency evaluation methods‎, ‎particularly in environments characterized by limited and uncertain data‎.

تازه های تحقیق
  • Introduces a novel network DEA framework integrating Liu’s uncertainty theory to effectively address data imprecision and inherent uncertainty.
  • Extends the conventional three-stage network DEA model into a flexible, multi-stage (p-stage) framework capable of modeling increasingly complex, interconnected systems under uncertainty.
  • Demonstrates the model’s robustness and practical utility through two comprehensive real-world case studies in the banking industry, emphasizing its applicability with limited and subjective data.
  • Highlights the model’s superior ability to incorporate expert opinions and subjective judgments, improving decision-making in environments with scarce or imprecise data.
  • Validates the framework’s capacity for accurate efficiency assessment and resource allocation despite data uncertainty, supporting strategic decision-making in diverse complex systems.
  • Showcases the potential of the proposed approach to enhance traditional DEA methodologies by effectively capturing data variability and uncertainty in multi-agent and multi-stage environments.
کلیدواژه‌ها
Uncertainty theory‎؛ ‎Network DEA‎؛ ‎Efficiency‎؛ ‎Banking system
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