Research on Fresh Pre-Positioning Warehouse Layout Based on Spatial Data Mining

Abstract

With the development of e-commerce logistics in China, online shopping has become increasingly popular among consumers, and the fresh e-commerce industry has shifted to a prepositioning warehouse distribution model closer to consumers. This study starts from consumer demands and considers the spatial distribution characteristics of consumers. Through cluster analysis, demand points with high consumer density are identified, and potential locations are determined through two rounds of clustering. Drawing insights from the warehousing and distribution models of Freshippo and MissFresh in Wuhan and Nanjing, four classification algorithms are compared to determine the optimal pre-positioning warehouse distribution model for the enterprise in this study. A multi-objective optimization model is established, aiming to minimize enterprise costs and maximize customer satisfaction, and the optimal site selection plan is obtained. By determining the number and locations of pre-positioning warehouses through the optimal solution, the enterprise can achieve maximum investment returns in the spatial layout of these warehouses.INDEX TERMS pre-positioning warehouse layout; cluster analysis; classification algorithms, NSGA-II algorithm.