针对物流装车中强异构型货物对装载效率的影响及其装载结果对车辆稳定性的影响，基于拟人装载策略和改进遗传算法提出了一种以车厢空间利用率最大及车辆重心偏移量最小为目标的三维装箱算法。该算法考虑了货物种类、体积、承重能力、不重叠等多种现实约束条件，融入了KD树（KD-tree）结构来优化算法的运行速度，采用了贝叶斯参数寻优来获得算法的最佳参数。该算法应用于某物流企业某批次装车优化最高达到了93.63%的空间利用率；采用Bischoff & Ratcliff标准算例进行测试，空间利用率最高可达到93.03%。经验证，该算法能够有效解决强异构货物的装箱问题，为物流企业提供较好的装载解决方案。

In view of the influence of strong heterogeneous goods on loading efficiency and the influence of loading results on vehicle stability in logistics loading, a three-dimensional packing algorithm with the goal of maximizing the utilization rate of compartment space and the minimum offset of vehicle center of gravity was proposed based on anthropomorphic loading strategy and improved genetic algorithm. The algorithm considered a variety of realistic constraints such as cargo type, volume, load-bearing capacity, non-overlapping, etc., and integrated the KD-tree structure to optimize the running speed of the algorithm, and useed Bayesian parameter optimization to obtain the best parameters of the algorithm. The algorithm was applied to a logistics enterprise to optimize the loading of a certain batch, and the maximum space utilization rate was 93.63%. The Bischoff & Ratcliff standard example was used for testing, and the space utilization rate was up to 93.03%. It has been verified that the algorithm can effectively solve the packing problem of strong heterogeneous goods and provide a better loading solution for logistics enterprises.