Optimizing Large-Scale Distribution Center Locations During the COVID-19 Quarantine

The pneumonia caused by COVID-19 is spreading worldwide, threatening human health and life. In the last three years, China has taken a series of effective measures to prevent the spread of the virus, where a core measure is the stay-home quarantine imposed in infected communities. The quarantine can effectively reduce physical contacts and transmission risk, however, it encounters several difficulties especially the guarantee of living materials. In order to ensure the quantity and freshness of living materials such as vegetables and fruits, it is necessary to construct multi-level logistics distribution centers, where the selection of locations for these centers becomes a vital issue. Such facility location problems are challenging in terms of both modeling and optimization, especially when facing the millions of residents and thousands of communities in a city commonly existing in China. In this study, we build a large-scale multi-objective optimization model for the distribution center location problem, and solve it via state-of-the-art sparse evolutionary algorithms. The experimental results verify that the center locations obtained by our approach can save human resources while reducing the risk of virus propagation.