基于改进粒子群算法的航空行李在线装载优化

目的 为解决航空行李自动装卸中关键装载算法问题,实现航空行李自动装卸,同时满足流水作业的实际需要.方法 基于关键点装载策略,提出一种以装载空间利用率为优化目标,考虑行李质量、体积及装载顺序等约束条件的改进粒子群算法.首先,通过关键点法输出流水线上待装载行李的全部可放点序列,然后根据约束条件重新定义粒子群算法的速度与位置,以空间利用率为适应度函数进行迭代寻优,输出全局最优解,实现对装载位置与姿态的优化.结果 实验部分采用真实行李数据对算法进行仿真验证表明,改进粒子群算法优化后可将箱体空间利用率提高了10.8％,平均规划布局效率提高了26.5％.结论 提出的装载算法能够有效地解决实际行李装载问题,为行李流水作业的货物装载提供理论依据及参考.

Online Luggage Loading Optimization Based on Improved Particle Swarm Algorithm

The work aims to solve the key loading algorithm problem in the automatic loading and unloading of air luggage, realize automatic loading and unloading of air luggage and meet the actual needs of flow operation. Based on the key point loading strategy, an improved particle swarm optimization (PSO) algorithm that considered constraints such as the weight, volume and loading order of luggage was proposed with the utilization of loading space as the optimization objective. Firstly, the key point method was used to output all the point sequences of the bags to be loaded on the pipeline. Then, the speed and position of the particle swarm optimization algorithm were redefined according to the constraint conditions. The space utilization was used as the fitness function for iterative optimization, and the global optimal solution was output to optimize the loading position and attitude. In the experimental part, the real luggage data was used to verify the algorithm. The results showed that the improved particle swarm optimization algorithm could improve the box space utilization by 10.8% and the average layout efficiency by 26.5%. The proposed loading algorithm can effectively solve the actual luggage packing problem, and provide a theoretical basis and reference for the cargo loading of luggage flow process.