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Enhancing disaster housing recovery through planning: A genetic algorithm approach for resource allocation.

Eduardo Landaeta¹

¹Institute for Coastal Adaptation and Resilience, Old Dominion University, Norfolk, Virginia. ORCID: https://orcid.org/0000-0002-6888-5314.

Journal of Emergency Management (Weston, Mass.)

|September 5, 2025

Summary

This study uses genetic algorithms (GAs) to optimize disaster housing recovery (DHR) resource allocation for flood-prone communities. GA-driven planning enhances resilience and community autonomy, improving long-term recovery outcomes.

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Area of Science:

Environmental science and urban planning, focusing on climate change adaptation and disaster resilience.

Background:

Climate change exacerbates flooding, causing displacement and financial instability in vulnerable communities.
Effective disaster housing recovery (DHR) is critical, especially in resource-constrained regions.
Optimizing resource allocation for DHR is essential for long-term community resilience.

Purpose of the Study:

To present an innovative strategy for improving DHR planning and execution using genetic algorithms (GAs).
To focus on the role of Long-Term Recovery Groups (LTRGs) and community engagement in achieving sustainable recovery.
To evaluate the efficacy of GAs in optimizing DHR resource allocation considering multiple criteria.

Main Methods:

Development and application of a GA model for DHR resource allocation.
Evaluation of hypotheses concerning optimization, LTRG preparedness, and community autonomy.
Analysis of resource allocation balancing cost-effectiveness, housing coverage, and stakeholder needs.

Main Results:

GA-driven planning significantly improves resource allocation decisions in DHR.
The model efficiently navigates complex resource allocation challenges.
Enhanced community autonomy and preparedness of Long-Term Recovery Groups (LTRGs) were observed.

Conclusions:

Genetic algorithms offer a powerful tool for optimizing complex disaster housing recovery processes.
GA-based strategies enhance community resilience and promote effective long-term recovery.
Findings provide valuable insights for policymakers and disaster response teams aiming to improve DHR.