目的 为了解决当前多数装箱算法未考虑装载顺序约束，不能有效解决航空货物装载的实际应用问题，开展多箱装载优化算法研究。方法 首先采用K-means算法对货物进行预分配，将聚类簇特性相同的货物分配到同一个集装箱；然后利用极点法得到极点序列，结合遗传算法进行寻优产生各集装箱的布局方案。结果 对某机场物流公司的160件货物数据进行实验，并与连续性策略进行比较，证明了含预分配策略的极点装载法能够有效避免个别集装箱利用率偏低的情况，并将集装箱利用率的总体方差降到0.51。结论 算法在考虑货物装载顺序约束的情况下，在多箱装载优化中能实现货物的合理分配，具有较好的工程应用性。

The work aims to research a multi-container loading optimization algorithm, so as to solve the problem that most of the current packaging algorithms do not consider the loading order constraints and cannot effectively solve the problem of practical application of air cargo loading. First, the K-Means algorithm was used for cargo pre-allocation, and the cargoes with the same clustering characteristics were assigned to the same container. Then, the extreme point order was obtained by extreme points (Eps), and the genetic algorithm was used to optimize the layout scheme of each container. 160 cargo data of an airport logistics company were used for experiments and compared with the continuity strategy, which proved that the Eps loading method with pre-allocation strategy could effectively avoid the low utilization of individual container and make the population variance of container utilization drop to 0.51. Considering the constraints of cargo loading order, the algorithm can realize the rational allocation of cargo in multi-container loading optimization and has better engineering applicability.