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Area of Science:

  • Computational Logistics
  • Artificial Intelligence
  • Operations Research

Background:

  • Container terminal operations are complex, nonlinear, and coupled, impacting global logistics.
  • Accurate prediction of liner berthing time (LBT) is crucial for terminal efficiency and carbon footprint management.

Purpose of the Study:

  • To propose and validate a container terminal-oriented neural-physical fusion computation (CTO-NPFC) framework.
  • To develop a deep learning model core computing architecture (DLM-CCA) for precise LBT prediction.

Main Methods:

  • Utilized a deep neural networks model (DLM-CCA) within the CTO-NPFC paradigm.
  • Employed TensorFlow 2.3 and the tsfresh package for feature extraction.
  • Trained and evaluated the model on five years of quayside running data from a Chinese container terminal.

Main Results:

  • The DLM-CCA achieved agile, efficient, and flexible LBT forecasting performance.
  • The model demonstrated excellent prediction accuracy with low computational costs on standard hardware.
  • Successfully validated the feasibility of the CTO-NPFC approach.

Conclusions:

  • The developed DLM-CCA effectively predicts LBT, enhancing container terminal logistics.
  • CTO-NPFC offers a viable and credible paradigm for addressing complex terminal operations.
  • This research lays the groundwork for optimizing task scheduling and resource allocation in container terminals.