Optimizing resource allocation and coordination in case of natural disasters using a game theory

¹ World College South East Asia (UWCSEA), Dover Campus
² On My Own Technology (OMOTEC), Mumbai, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Natural disasters, such as earthquakes, floods, and hurricanes, cause immense damage to lives and infrastructure, with traditional resource allocation methods often failing to address the urgency and equity required in disaster response. This research proposes a game-theoretic model to optimize resource distribution among key stakeholders, including government representatives, NGOs, private sectors, and affected populations. The model treats resource allocation as a strategic interaction, aiming to minimize unmet needs and maximize fairness. Using a cooperative approach, it incorporates equilibrium analysis and coalition formation to improve coordination. The Analytical Hierarchy Process (AHP) prioritizes regions based on disaster severity, population density, and accessibility, while a linear programming model ensures optimal allocation under constraints. A case study involving three regions with varying resource needs demonstrated the model’s effectiveness in addressing shortages for two regions, though challenges persist for the most severely affected area. Results show that government representatives play a pivotal role, contributing the majority of resources with the highest operational efficiency (utility score >0.8). The study underscores the utility of game theory in disaster management, highlighting its potential to ensure timely, equitable, and efficient resource distribution. Future research could enhance the model by incorporating real-time data, multi-modal logistics, and dynamic resilience factors to address the evolving complexities of disaster scenarios. This framework offers a practical and scalable solution for improving disaster response and reducing the suffering of affected populations.