北京航空航天大学学报 ›› 2022, Vol. 48 ›› Issue (5): 747-755.doi: 10.13700/j.bh.1001-5965.2020.0665

• 论文 • 上一篇    下一篇

基于混合遗传算法的多箱型集装箱装载问题分析

张长勇, 刘佳瑜   

  1. 中国民航大学 电子信息与自动化学院, 天津 300300
  • 收稿日期:2020-11-30 发布日期:2022-05-30
  • 通讯作者: 张长勇 E-mail:cyzhang@cauc.edu.cn
  • 基金资助:
    国家自然科学基金(51707195);中央高校基本科研业务费专项资金(3122016A009)

Multi-box container loading problem based on hybrid genetic algorithm

ZHANG Changyong, LIU Jiayu   

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
  • Received:2020-11-30 Published:2022-05-30
  • Supported by:
    National Natural Science Foundation of China (51707195);the Fundamental Research Funds for the Central Universities (3122016A009)

摘要: 航空多箱型集装箱装载是实现快速、高效、安全航空货物运输的重要环节。针对现实约束条件下多种货物和箱型的集装箱多箱装载优化问题,搭建数学优化模型,提出一种求解货物装载布局方案的混合遗传算法,以达到充分利用集装箱装载空间的目的。采用三段式编码确定货物装载顺序、货物放置状态及集装箱编号,随机产生初始种群;在常规选择操作中加入最佳个体保护策略,并将重心、不重叠、承重约束考虑到适应度函数中,以此来评价解的优劣;加入模拟退火算子,用其突跳性避免遗传陷入局部最优的情况,进一步提高优化效果。通过算例对比表明,所提算法在满足多种约束条件下仍能保持较高的体积利用率,能够很好地解决强弱异构货物的装载;采用具体货物数据进一步验证算法的可行性与适用性,4种航空集装箱的平均体积利用率高于82%,表明所提算法能够有效解决规则和不规则多箱型集装箱的货物装载问题,具有较好的工程应用价值。

关键词: 集装箱装载, 多箱型, 混合遗传算法, 强异构货物, 模拟退火, 可视化

Abstract: Aviation multi-box container loading is an important link to realize fast, efficient and safe air cargo transportation. Aimed at the multi-box container loading optimization problem of multiple cargoes and container types under realistic constraints, a mathematical optimization model is built and a hybrid genetic algorithm is proposed to solve the cargo loading layout scheme, so as to make full use of the container loading space. The three-stage code is used to determine the loading order, cargo placement status and container number to generate the initial population randomly. The optimal solution protection strategy is added in the conventional selection operation, and the center of gravity, non-overlapping and load-bearing constraints are taken into account in the fitness function to evaluate the solution. The simulated annealing operator is added to avoid falling into the local optimum by using its jump property, which further improves the optimization effect. Through the comparison of examples, it shows that the proposed algorithm can still maintain a high volume utilization rate under various constraints, and can well solve the loading of strong and weak heterogeneous cargoes. The feasibility and applicability of the algorithm are further verified by using specific cargo data. The average volume utilization rate of four kinds of air container is higher than 82%, which shows that the proposed algorithm can effectively solve the cargo loading problem of regular and irregular multi-box containers, and has good engineering application value.

Key words: container loading, multi-box, hybrid genetic algorithm, strong heterogeneous cargo, simulated annealing, visualization

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