Optimizing Resource Allocation to Mitigate the Risk of Disruptive Events in Homeland Security and Emergency Management
Parastoo Akbari, Cameron A. MacKenzie
TL;DR
The paper addresses risk-aware allocation of homeland security resources across multiple hazards under a budget. It develops an optimization model that minimizes the expected, multi-dimensional risk, defined as $ \sum_{i=1}^n p_i(\mathbf{x}) \big( \sum_{j=1}^m w_j f_{ij}(\mathbf{x}) \big)$ with $p_i(\mathbf{x}) = \hat{p}_i \prod_{k=1}^K \alpha_{ik}^{x_k}$ and $f_{ij}(\mathbf{x}) = \hat{f}_{ij} \prod_{k=1}^K \beta_{ijk}^{x_k}$, under a budget constraint $\mathbf{c}^\intercal \mathbf{x} \le B$ and binary decision variables $x_k$. The framework is applied to Iowa (16 hazards, 6 consequences, 52 mitigation projects) using THIRA data, NOAA Storm Events, NID, and FEMA HMA to estimate risks and project effectiveness; results show substantial risk reductions with budget increases and identify Project 20 as highly cost-effective. The findings suggest that prioritizing high-effect, multi-hazard projects yields the greatest ROI, though returns exhibit diminishing gains beyond moderate budgets, offering a practical tool for policymakers and a foundation for extending the model to uncertainty and dynamic, time-evolving planning.
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.