Intelligent optimization of e-commerce order packing using deep reinforcement learning with heuristic strategies

The rapid expansion of e-commerce has intensified demands for efficient logistics, particularly in optimizing three-dimensional bin packing (3D-BPP) to balance space utilization, operational costs, and sustainability. Traditional methods often fail to address the dynamic, multi-constrained nature of e-commerce orders, which involve diverse item combinations, real-time decision-making, and complex practical constraints. This study proposes a hybrid framework that integrates deep reinforcement learning (DRL) with heuristic strategies to tackle these challenges. We first formulate a comprehensive mathematical model for 3D-BPP that explicitly incorporates rotation, boundary, and non-overlapping constraints. Building on this foundation, we develop a heuristic strategy system with five operator components for bin selection, item grouping, packing sequence, position selection, and orientation determination. To enhance adaptability, we introduce two DRL algorithms: the Order Packing Optimization DRL (OPO-DRL) for dynamic item sequencing and the Packing Combination Strategy DRL (PCS-DRL) for adaptive operator selection. The hybrid framework synergizes DRL’s learning capabilities with heuristic efficiency, enabling real-time adjustments to varying order patterns and bin specifications. Experimental validation using real-world data from JD.com demonstrates significant improvements, achieving an average packing rate of 68.60% with computation times of 0.16 s per order, outperforming state-of-the-art methods. Statistical analysis confirms significant improvements in both solution quality and computational efficiency compared to existing approaches. This work bridges theoretical optimization with operational realities, providing a scalable solution for modern warehouse automation and intelligent logistics systems.