<![CDATA[Humanitarian logistics plays a crucial role in disaster management, yet it faces persistent challenges in ensuring equitable and efficient aid distribution. These challenges include high operational costs, inequitable resource allocation, and limited adaptability to dynamic disaster conditions. Existing models, either cost-based or priority-based, fail to balance these competing demands effectively. This research addresses this gap by developing a hybrid multi-objective optimization model that integrates cost minimization and priority-based resource allocation. The primary objective of this study is to optimize aid distribution in disaster-affected regions, ensuring both efficiency and fairness. The model employs a quantitative research approach, leveraging mathematical programming to simulate disaster scenarios. Key variables include operational costs, delivery times, demand levels, and priority rankings, collected from hypothetical disaster data. The results reveal that the hybrid model significantly outperforms conventional approaches. It achieves up to a 15% reduction in operational costs and a 25% improvement in aid coverage for high-priority regions. Furthermore, it balances resource allocation effectively across regions with varying levels of need. The discussion emphasizes the model's adaptability and scalability, offering practical solutions for disaster relief operations. In conclusion, the hybrid model presents a robust framework for humanitarian logistics, addressing inefficiencies and inequities in aid distribution. This research impacts policy-making and operational planning, paving the way for more effective disaster management strategies.]]>
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