B2C E-Commerce Logistics Network optimization Based on Constrained Clustering Algorithm

With the improvement of residents' consumption level, attention to quality has increasingly become an important reference standard for consumers to choose ecommerce, making e-commerce gradually shift from “price war” to “service war The research on B2C e-commerce logistics network optimization based on constrained clustering algorithm is a research work in the field of information technology and engineering. The main goal of this research is to develop a new algorithm to optimize the performance of B2C e-commerce logistics network. This research also focuses on developing an effective method to identify the constraints that affect the performance of B2C e-commerce logistics network. In order to optimize the performance of B2C ecommerce logistics network, constraint clustering method is used to identify all constraints that affect the performance of B. In B2C e-commerce logistics network optimization, constrained clustering algorithms can also optimize delivery paths. By preset constraints such as starting point, ending point, and necessary passing point, constrained clustering algorithms can optimize delivery routes, reduce transportation time and costs while ensuring the same quality of consumer service. In addition, constrained clustering algorithms can also predict logistics demand, formulate more reasonable logistics plans, and dynamically adjust according to actual situations.