A feature-extraction-based lightweight convolutional and recurrent neural networks adaptive computing model for container terminal liner handling volume forecasting
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Publication:2232683
DOI10.1155/2021/6721564zbMath1471.90028OpenAlexW3197948966MaRDI QIDQ2232683
Publication date: 8 October 2021
Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2021/6721564
Transportation, logistics and supply chain management (90B06) Discrete location and assignment (90B80)
Uses Software
Cites Work
- Tug scheduling for hinterland barge transport: a branch-and-price approach
- Stochastic modeling of parallel process flows in intra-logistics systems: applications in container terminals and compact storage systems
- A comprehensive and systematic look up into deep learning based object detection techniques: a review
- Deep learning assisted heuristic tree search for the container pre-marshalling problem
- Pre-processing a container yard under limited available time
- Terminal allocation problem in a transshipment hub considering bunker consumption
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