As the frequency, duration, and intensity of heat waves have been increasing in recent decades, the effective and efficient allocation of cooling shelters has become a significant issue in many cities. This study presents an integer programming model for allocating cooling shelters with the two conflicting objectives of maximizing coverage for the heat-vulnerable population and minimizing total operating cost of the cooling shelters. The temperature-humidity index is included in the model to reflect the weather conditions that affect heat waves. We also introduce data analysis procedures using real-time floating population data so as to track the hourly number and locations of individuals in the heat-vulnerable population. The proposed model is then validated with an application to Ulsan Metropolitan City in the Republic of Korea in which heat-vulnerable people are assigned to existing and potential cooling shelters. Given the condition of restricted budgets, we categorize and prioritize heat-vulnerable people into several groups using a clustering method and heat vulnerability index, and we suggest effective policy recommendations, so the most vulnerable people are provided cooling services first. In addition, we perform a sensitivity analysis on weather conditions, travel distance, electricity cost, and percentage of heatvulnerable population served by cooling shelters, so policy makers can be prepared to respond quickly to the various factors that can change during a heat wave and ultimately reduce heatrelated morbidity and mortality.