A Hybrid Approach for Emergency Supplies Site Selection Optimization: A Case Study of Changchun City Using Cellular Automata and WK-Means Clustering

Since 2022, several large-scale epidemic outbreaks have occurred in various parts of the world, posing a risk of interruption of emergency supplies and potential disruptions in the supply chain. Therefore, selecting appropriate emergency supply points is crucial to mitigate these risks. The selection and distribution of emergency supplies must consider multiple objectives, such as labor and economic costs, personnel aggregation, and mobility. To address these challenges, this paper proposed a hybrid approach that combines cellular automata and K-means algorithms to optimize emergency supply site selection. Using Changchun City as an example, we conducted simulations of this method, and the results show that the proposed double-layer hybrid algorithm can significantly reduce the required transportation capacity by 33.67% compared to the actual local delivery point plan. Additionally, we defined a plan for the orderly distribution of future residential emergency supplies in this work.