文章摘要
王帅,洪振宇.基于强化学习的机场行李装箱优化方法[J].包装工程,2022,43(3):257-263.
WANG Shuai,HONG Zhen-yu.Optimization Method of Baggage Packing in Airport Based on Reinforcement Learning[J].Packaging Engineering,2022,43(3):257-263.
基于强化学习的机场行李装箱优化方法
Optimization Method of Baggage Packing in Airport Based on Reinforcement Learning
投稿时间:2021-03-26  
DOI:10.19554/j.cnki.1001-3563.2022.03.032
中文关键词: 行李码放  三维装箱  最大剩余空间  强化学习
英文关键词: baggage placement  three-dimensional packing  empty maximal spaces  reinforcement learning
基金项目:中央高校基本科研业务费项目中国民航大学专项(3122018D038)
作者单位
王帅 中国民航大学 航空工程学院天津 300300 
洪振宇 中国民航大学 航空工程学院天津 300300 
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中文摘要:
      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。
英文摘要:
      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.
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