基于无监督深度融合机制的货物在线装箱算法

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。

Online Cargo Packing Algorithm Based on Unsupervised Deep Fusion Mechanism

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.