A Hybrid Optimization Model for Shelter Location and Materials Assignment in Earthquakes Based on Evacuation Behavior

A critical gap in disaster response persists between optimized logistical plans and the complex, often unpredictable evacuation behaviors of affected populations. Traditional location-allocation models often prove insufficient in practice, as they typically treat evacuees as homogeneous, rational actors, thereby overlooking the dynamic, hierarchical needs that govern decision-making on the ground. This study addresses this gap by introducing a hybrid optimization framework that systematically translates data-driven behavioral insights into a multi-objective model for shelter location and material assignment. Rather than modeling microscopic routing behaviors, our methodological innovation lies in a two-stage pipeline. First, we integrate thematic analysis of large-scale social media data (3400 relevant posts) from recent earthquakes to extract and quantify time-varying demand patterns for three core needs: essential survival, medical treatment, and psychological needs. Second, these empirically derived priority dynamics are embedded as dynamic parameters within the optimization model. Validated against the 2013 Ya'an earthquake case study and compared with classical baselines (e.g., the p-median model), the framework reduces the average evacuation distance while ensuring feasible access for heterogeneous vulnerable groups. Concurrently, it enhances resource equity by dynamically prioritizing material allocation based on the evolving urgency of evacuee needs. This research thus contributes not only a specific decision-support tool but also a generalizable paradigm for bridging descriptive behavioral data with prescriptive operations models, offering a more realistic foundation for risk analysis in humanitarian logistics.