Artificial Intelligence Based Multilevel Optimization Models for Complex Decision Systems

Abstract

Complex decision systems, such as supply chains, smart cities, and healthcare networks, are characterized by hierarchical structures, dynamic environments, and high levels of uncertainty, making them difficult to optimize using traditional methods. Conventional optimization approaches, which are typically static and single-level, are limited in their ability to handle interdependent decisions and rapidly changing conditions. This study proposes an Artificial Intelligence-based multilevel optimization model to address these challenges by integrating hierarchical optimization with advanced AI techniques. The proposed framework combines multilevel optimization encompassing strategic, tactical, and operational decision layers with Artificial Intelligence methods, including neural networks for prediction, reinforcement learning for adaptive decision-making, and genetic algorithms for global optimization. A simulation-based methodology is employed to model complex environments and evaluate system performance under various scenarios. The results demonstrate that the proposed model significantly outperforms traditional optimization approaches. It achieves higher accuracy, faster convergence, and greater adaptability in dynamic and uncertain environments. Sensitivity analysis confirms the robustness of the model under varying conditions, while scalability tests indicate its effectiveness in handling large-scale systems. These findings highlight the advantages of integrating AI with multilevel optimization for complex decision-making. It offers both theoretical and practical implications for improving decision-making in complex systems. Future research is recommended to enhance computational efficiency, improve model interpretability, and validate the framework through real-world applications across various domains.