<![CDATA[Flooding continues to pose a major challenge in urban areas of developing countries, necessitating effective humanitarian logistics, particularly during the preparedness phase. However, the absence of an optimized aid post-placement strategy remains a significant barrier to efficient aid distribution. This study presents a humanitarian logistics preparedness model that integrates K-means clustering with the Set Covering Location Problem (SCLP) to enhance aid post allocation. The clustering technique categorizes affected areas based on geographical proximity and flood severity, ensuring a more effective resource distribution. Compared to traditional district-based grouping, the proposed approach improves resource allocation efficiency by 5.61%. The model was tested using Excel Solver under varying disaster impact scenarios. Findings indicate that in the 100% affected scenario, 37 villages were covered by 18 aid posts, with round-trip distance of 74.72 km. In the 50% affected scenario, 19 villages were served by 8 aid posts, with the distance reduced to 33.65 km. The results highlight the importance of integrating clustering techniques with optimization models to improve data-driven humanitarian logistics planning. The proposed framework facilitates systematic aid distribution, making it a scalable solution that can be adapted to other disaster-prone regions to strengthen flood disaster response strategies]]>
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