Research on Heterogeneous Container Loading Strategies Based on a Three-Stage Heuristic Algorithm

Efficient configuration of multi-form cargo in limited container space is a critical challenge for modern logistics enterprises. This study aims to minimize transportation costs and leftover cargo mass under complex packing constraints, including weight limits, volume capacities, and stacking stability. We propose a three-stage heuristic optimization model—”Classify-Layer-Mix”—to coordinate the loading of heterogeneous cylindrical and rectangular items. Specifically, a honeycomb arrangement strategy is employed to optimize the bottom area utilization for cylinders, while a stability-based stacking rule (“rectangles below cylinders”) ensures loading safety. The mathematical model incorporates 0-1 integer programming to balance transportation fees and leftover penalties. Experimental simulations using Python demonstrate that the proposed model effectively reduces leftover cargo mass and maintains costs within an optimal range. For large-scale datasets, the algorithm exhibits high robustness and computational efficiency, providing a practical and scalable decision-making tool for intelligent logistics scheduling and container loading optimization.