Location optimization of cold chain logistics parks based on Bayesian probability theory and K-means clustering analysis in China

The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites.