Resilient Decision -Making: The Role of Approximate Dynamic Programming in Disaster Management

Background and Objectives: Disaster recovery and resilience planning require robust decision-making models that can handle uncertainty, optimize resource allocation, and enhance system restoration. The Approximate Dynamic Programming (ADP) Framework has emerged as a powerful approach for managing large-scale, complex decision-making processes in disaster risk management, industrial engineering, and community planning. Methods: This article provides a comprehensive review of the ADP framework, highlighting its theoretical foundations, methodological advancements, and practical applications in post-disaster recovery. ADP enables sequential decision-making by incorporating real-time data, probabilistic modeling, and optimization techniques to improve infrastructure restoration and community resilience. Findings: The review explores its advantages over traditional deterministic models, its integration with artificial intelligence and machine learning, and its scalability for real-world implementation. Conclusion: Additionally, we discuss challenges in computational complexity, data availability, and policy adoption, along with future research directions to enhance the framework’s effectiveness. By bridging the gap between theory and practice, this review underscores the transformative potential of ADP in optimizing disaster response and resilience planning.

کلیدواژه ها:

Approximate Dynamic Programming(ADP) ، Post-Disaster Recovery ، Resilient Decision-Making ، Infrastructure Restoration ، Optimization Techniques