文章摘要
张长勇,姚凯超,王彤.基于无监督深度融合机制的货物在线装箱算法[J].包装工程,2024,45(11):153-162.
ZHANG Changyong,YAO Kaichao,WANG Tong.Online Cargo Packing Algorithm Based on Unsupervised Deep Fusion Mechanism[J].Packaging Engineering,2024,45(11):153-162.
基于无监督深度融合机制的货物在线装箱算法
Online Cargo Packing Algorithm Based on Unsupervised Deep Fusion Mechanism
投稿时间:2023-10-18  
DOI:10.19554/j.cnki.1001-3563.2024.11.018
中文关键词: 在线三维装箱  无监督融合机制  马尔科夫决策  指针网络  蒙特卡洛树搜索
英文关键词: online 3D packing  unsupervised integration mechanism  Markovian decision  pointer network  Monte Carlo tree search
基金项目:中央高校高水平培育项目(3122023PY04)
作者单位
张长勇 中国民航大学 电子信息与自动化学院天津 300300 
姚凯超 中国民航大学 电子信息与自动化学院天津 300300 
王彤 中国民航大学 电子信息与自动化学院天津 300300 
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中文摘要:
      目的 针对当前三维装箱算法存在的模型鲁棒性差、泛化性弱、装载率低等问题,设计一种无监督融合机制的在线装箱算法。方法 充分考虑货物“即到即码”的实时性需求,以容器空间利用率为优化目标,基于无监督深度融合指针网络端到端学习模型框架,将在线三维装箱的码垛过程公式化地表述为马尔科夫决策过程,设计强化学习要素,并以深度强化学习算法为主,融入蒙特卡洛树搜索,对智能体的决策动作进行训练,以生成具有较优“学习”能力的在线三维装箱模型。结果 采用125种不同尺寸和方向随机生成货物数据集,并在7种约束条件下验证,实验结果表明,容器的平均利用率可达84.6%。结论 该算法的泛化性较好,且其装载率远优于当前效果较好的启发式算法、深度学习方法,为货物的在线装箱提供了理论依据及参考。
英文摘要:
      The work aims to design an on-line unsupervised integration algorithm, in order to solve the problems of poor model robustness, poor generalization and low loading rate in the existing 3D packing algorithm. In full consideration of the real-time premise of just-in-time cargo and with the container space utilization rate as the optimization goal, based on the end-to-end learning model framework of unsupervised deep fusion pointer network, the stacking process of online 3D packing was formulated as a Markovian decision-making process, to design reinforcement learning elements, and to give priority to the deep reinforcement learning algorithm. The decision-making actions of the agent were trained with the Monte Carlo tree search to generate an online three-dimensional boxing model with better learning ability. 125 randomly generated cargo data sets with different sizes and directions were tested under 7 constraint conditions. The experimental results showed that the average utilization rate of containers could reach 84.6%. The generalization of the algorithm is good, and the loading rate of the algorithm is much better than the current heuristic and depth learning method, providing theoretical basis and reference for on-line packing of cargo.
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