An Elderly-Centered Location-Allocation Model for Multi-Scenarios Flood Relief Distribution in Mae Chan, Chiang Rai, Thailand

Flooding remains a major challenge in Thailand, severely impacting many regions and highlighting the need for efficient disaster logistics, particularly for elderly residents who often cannot evacuate. This study integrates logistics optimization and geoinformatics to identify optimal locations for temporary Relief Distribution Centers (RDCs) that can deliver aid effectively. Household-level survey data and Kernel Density Analysis were used to map areas with high concentrations of elderly populations. The Particle Swarm Optimization (PSO) algorithm was then applied to determine which RDCs should operate to maximize elderly coverage, minimize total travel time, and optimize resource allocation. Analysis across four flood scenarios revealed a pronounced disparity in workload distribution: a limited subset of RDCs consistently absorbed the majority of demand, with top-performing centers serving 27–230 households, 92–446 elderly, and 266–646 non-elderly individuals, while low-load centers managed fewer than 60 households and under 150 elderly. Overall, 30–40% of RDCs handled over 60% of the total assistance burden, highlighting systemic inequities in service allocation. These findings emphasize the need for scenario-specific, evidence-based resource prioritization, reinforced logistical capacity, and strategic staffing in high-demand centers to prevent service bottlenecks, ensure equitable coverage, and maintain effective flood response for vulnerable populations.