An AI-based cross-border e-commerce logistics scheduling and stowage optimization method

Abstract

This invention discloses an AI-based method for cross-border e-commerce logistics scheduling and load optimization, belonging to the field of cross-border e-commerce logistics technology. It includes deep data preprocessing and priority scheduling, multi-mode backtracking search algorithm matching for quotations, physical and business dual verification, global multi-objective competitive optimization, end-to-end dynamic cost calculation, and instruction-based output and execution. This invention achieves three-dimensional loading optimization and dynamic load allocation through AI constraint solving algorithms, improving container space utilization to the algorithm's optimal level and shortening the manual container arrangement process from several hours to seconds. It enables rapid response to order and container changes, thereby improving the efficiency and space utilization of cross-border e-commerce logistics. Furthermore, by constructing a nested hash index and a dynamic cost engine, it traverses supplier quotations across the entire chain in milliseconds, uncovering low-price combinations that cannot be identified manually, reducing overall logistics costs, and increasing enterprise profitability, thus achieving optimal end-to-end costs.

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