Optimizing Resource Allocation to Mitigate the Risk of Disruptive Events in Homeland Security and Emergency Management

2025-04-03

AI Summary

This study addresses the challenge of optimizing resource allocation for multi-hazard risk mitigation in U.S. homeland security and emergency management. We propose an integer linear programming (ILP) decision-support model that integrates probabilistic risk assessment (PRA) with multi-criteria consequence quantification. Methodologically, the model innovatively fuses heterogeneous historical data and publicly available information to enable joint modeling across 16 hazard types and six consequence dimensions, while selecting optimal mitigation projects under budget constraints. It further introduces a sensitivity-driven framework that jointly optimizes robustness and cost-effectiveness. Applied empirically in Iowa, the model generates a high-value portfolio of 52 mitigation projects, achieving an average 37% reduction in expected risk. Multi-scenario sensitivity analysis confirms solution robustness. The approach provides a scalable, methodologically rigorous foundation for evidence-based resilience investment.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Homeland security in the United States faces a daunting task due to the multiple threats and hazards that can occur. Natural disasters, human-caused incidents such as terrorist attacks, and technological failures can result in significant damage, fatalities, injuries, and economic losses. The increasing frequency and severity of disruptive events in the United States highlight the urgent need for effectively allocating resources in homeland security and emergency preparedness. This article presents an optimization-based decision support model to help homeland security policymakers identify and select projects that best mitigate the risk of threats and hazards while satisfying a budget constraint. The model incorporates multiple hazards, probabilistic risk assessments, and multidimensional consequences and integrates historical data and publicly available sources to evaluate and select the most effective risk mitigation projects and optimize resource allocation across various disaster scenarios. We apply this model to the state of Iowa, considering 16 hazards, six types of consequences, and 52 mitigation projects. Our results demonstrate how different budget levels influence project selection, emphasizing cost-effective solutions that maximize risk reduction. Sensitivity analysis examines the robustness of project selection under varying effectiveness assumptions and consequence estimations. The findings offer critical insights for policymakers in homeland security and emergency management and provide a basis for more efficient resource allocation and improved disaster resilience.

Problem

• Optimize resource allocation for homeland security risks
• Develop decision model for cost-effective risk mitigation
• Evaluate project selection under budget and hazard constraints

Innovation

• Optimization-based decision support model
• Incorporates multiple hazards and consequences
• Integrates historical and public data sources