基于强化学习的机场行李装箱优化方法

目的针对因行李随旅客无序抵达而无法提前得知行李尺寸信息的机场行李装箱问题,以行李车的装箱空间利用率为优化目标,提出基于强化学习的行李在线装箱方法。方法首先,根据机场行李装箱的实际情况,建立行李装箱的数学模型;接着,针对行李在行李车内寻找合适装箱位置和姿态的问题,设计行李装箱位置选择方法和装箱姿态评价方法;最后,借助强化学习的"试错"学习模式,通过训练行李装箱模型获得行李在线装箱策略。结果在仿真实验中文中算法的行李车空间利用率能够达到82.9%,计算耗时0.39 s,这2项指标均优于机器学习算法。结论在求解机场行李在线装箱问题上具有较好的实用性。

Optimization Method of Baggage Packing in Airport Based on Reinforcement Learning

Aiming at the problem of luggage packing in airports where luggage size information cannot be known in advance due to the disordered arrival of luggage with passengers, an online luggage packing method based on reinforcement learning is proposed with the utilization of the packing space of the luggage trolley as the optimization goal. First, a mathematical model of luggage packing was established according to the actual situation of luggage packing at the airport. Then, aiming at the problem of finding a suitable packing position and posture for luggage in the luggage cart, a method for selecting luggage packing position and evaluating method for packing posture was designed. Finally, with the help of the "trial and error" learning mode of reinforcement learning, the online luggage packing strategy was obtained by training the luggage packing model. In simulation experiments, the utility rate of space in baggage cart can reach 82.9%, and the calculation time was 0.39s. Both were better than machine learning algorithms. It has good practicability in solving the airport luggage online packing problem.