Effective decision-making in complex scenarios involving multiple dimensions and uncertainty presents significant challenges for traditional models. In this paper, we introduce the Uncertain Multi-Dimensional Programming (UMDI-PRO) model, a comprehensive framework designed to optimize decision-making in uncertain and multidimensional environments. By integrating advanced techniques from multivariate analysis and multicriteria decision-making (MCDM), UMDI-PRO provides a robust approach for evaluating and selecting optimal alternatives. Compared to conventional MCDM approaches, UMDI-PRO exhibits marked improvements in handling trade-offs, processing imprecise data, and generating stable solutions under uncertainty. Through numerical experimentation and comparative analysis, the UMDI-PRO model demonstrates superior performance in managing conflicting objectives and uncertain data, surpassing traditional MCDM methods. Case studies in supply chain management, healthcare, and environmental management illustrate its wide applicability and effectiveness in real-world problem-solving. The model's adaptability, accuracy, and resilience make it a powerful tool for enhancing the quality of decisions in dynamic and uncertain environments.
Citation: Mohammed Ali Elleuch, Marwa Mallek, Jalel Euchi, Francisco-Silva Pinto, Ahmed Frikha. A novel framework for uncertain multidimensional decision-making: UMDI-PRO model for optimized solutions in complex environments[J]. Data Science in Finance and Economics, 2025, 5(4): 502-535. doi: 10.3934/DSFE.2025020
Effective decision-making in complex scenarios involving multiple dimensions and uncertainty presents significant challenges for traditional models. In this paper, we introduce the Uncertain Multi-Dimensional Programming (UMDI-PRO) model, a comprehensive framework designed to optimize decision-making in uncertain and multidimensional environments. By integrating advanced techniques from multivariate analysis and multicriteria decision-making (MCDM), UMDI-PRO provides a robust approach for evaluating and selecting optimal alternatives. Compared to conventional MCDM approaches, UMDI-PRO exhibits marked improvements in handling trade-offs, processing imprecise data, and generating stable solutions under uncertainty. Through numerical experimentation and comparative analysis, the UMDI-PRO model demonstrates superior performance in managing conflicting objectives and uncertain data, surpassing traditional MCDM methods. Case studies in supply chain management, healthcare, and environmental management illustrate its wide applicability and effectiveness in real-world problem-solving. The model's adaptability, accuracy, and resilience make it a powerful tool for enhancing the quality of decisions in dynamic and uncertain environments.
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Figures(5) / Tables(5)
Mohammed Ali Elleuch, Marwa Mallek, Jalel Euchi, Francisco-Silva Pinto, Ahmed Frikha. A novel framework for uncertain multidimensional decision-making: UMDI-PRO model for optimized solutions in complex environments[J]. Data Science in Finance and Economics, 2025, 5(4): 502-535. doi: 10.3934/DSFE.2025020
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