.png){kind=icon}
返回
Container Rehandling Probability Prediction Model Based on Seq2Seq Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TR.2024.3392919.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the strong uncertainty in the retrieval order of containers and the complex coupling relationship between container yard production equipment, it is challenging for yards to formulate an appropriate slot allocation strategy to control the proportion of rehandling containers. Meanwhile, the unpredictable performance of the slot allocation strategy results in the yard lacking the means to adjust the allocation strategy. To address these issues, an efficient container rehandling probability prediction model based on deep learning has been proposed to assist yards in formulating and adjusting slot allocation strategy. Moreover, we design a container slot allocation strategy driven by the predictive container rehandling probability for reducing the proportion of rehandling container at yards. Extensive experiments on the container storage dataset demonstrate that: 1) the prediction model based on deep learning enables to efficiently and precisely predict the container rehandling, 2) taking Seq2Seq network as the prediction layer of model outperforms other deep sequence models on MSE, MAE, and accuracy, and 3) the slot allocation strategy based on the predictive container rehandling probability can effectively reduce the probability of the rehandling container.
Keyword:
Containers
Predictive models
Resource management
Mathematical models
Indexes
Deep learning
Stacking
Container rehandling
deep learning
rehandling probability prediction
Seq2Seq network
slot allocation
期刊
IF:
5.7
论文数:
2.8K
被引数:
8.5K
机构
引用论文
Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries用于锂离子电池充电状态估计的堆叠双向长短期记忆网络.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏
A new seq2seq architecture for hourly runoff prediction using historical rainfall and runoff as inputJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏
查看更多内容