Integrated Optimization of Location, Delivery Mode, and Information in Cross-Border Last-Mile Logistics

Abstract

This study addresses the industry pain points of high last-mile delivery costs, unstable delivery times, and lack of transparency in North American cross-border e-commerce logistics. It also considers the inadequacy of existing delivery network optimization models in adapting to the unique characteristics of cross-border scenarios. A hierarchical last-mile delivery network optimization model integrating “location - mode - information” has been developed. This model improves the traditional K-means clustering algorithm by incorporating customs clearance convenience into the overseas warehouse location index system. It dynamically matches delivery modes based on order timeliness, value, and weight attributes and builds a cross-border delivery information collaboration system using blockchain technology. Comparative analysis of operational data before and after model optimization in the northeastern region of a leading North American cross-border e-commerce platform reveals a 30.5% reduction in per-order delivery costs, a decrease in standard delivery time from 72 to 48 hours, and an increase in next-day delivery fulfillment rate to 92%. Customer experience-related indicators have also significantly improved. Expansion tests demonstrate the model’s adaptability to the Canadian and Mexican markets.