目的 以航空货运背景下流水线上货物的装箱问题为研究对象，旨在最大化地利用集装箱装载空间。方法 在考虑货物装载顺序、质量、体积、不重叠、稳定性等一系列现实约束的条件下，建立集装箱堆码模型，设计一种拟人启发式与遗传相结合的组合启发式算法。首先通过设定规则并赋予权重，得到有序可放置点集合，采用拟人启发式算法构建货物装载策略，得到初始装箱方案;然后通过遗传算法中的交叉、变异操作对方案进行寻优，在可行方案中选出集装箱空间利用率最大的装箱方案;最后采用某机场物流公司的实际货物数据进行实验，并实现装载方案的可视化。结果 相较于混合模拟退火算法，组合启发式算法收敛性好，搜索速度快，能够在较短的时间内得到空间利用率较高的装载方案，货物垛形规划更为紧凑，稳定性更高。结论 所提算法对于不同种类的货物有着更好的适应性，能够实现强异构货物的合理布局，保证了货物装载过程中垛形的稳定性，可为航空货物装箱问题的工程应用提供参考。

The work aims to maximize the use of container loading space with the packing problem of goods on the assembly line under the background of air freight as the research object. Considering a series of realistic constraints such as cargo loading order, quality, volume, non-overlap, stability, etc., a container stacking model was established, and a combined heuristic algorithm combining anthropomorphic heuristics and genetics was designed. First, by setting rules and assigning weights, an ordered set of peaceable points was obtained, and the anthropomorphic heuristic algorithm was used to construct the cargo loading strategy to obtain the initial packing solution; then the solution was optimized through crossover and mutation operations in the genetic algorithm. Among the feasible schemes, the packing scheme with the largest container space utilization rate was selected; finally, the actual cargo data of an airport logistics company was used for the experiment, and the loading scheme was visualized. Compared with the hybrid simulated annealing algorithm, the combined heuristic algorithm had good convergence, fast search speed, and can obtain a loading solution with a high space utilization rate in a short period of time, and the cargo stack planning was more compact and more stable. The proposed algorithm has better adaptability to different kinds of cargoes, can realize the reasonable layout of strong heterogeneous cargoes, guarantee the stability of the stacking shape during cargo loading, and provide a reference for the engineering application of air cargo packing.