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As global container transportation grows, the complexity and uncertainty of multi-equipment scheduling in ports demand higher operational efficiency in automated terminals. This paper presents an intelligent scheduling optimization model integrating the Quantum Co-evolutionary Bat Algorithm (QCEBA) and deep learning, focusing on the joint scheduling of quay cranes, AGVs, and yard cranes. The model leverages quantum computing's global search capability and deep learning's dynamic prediction function to enhance multi-equipment collaboration. This approach effectively addresses multi-objective scheduling complexity and provides a theoretical foundation for digital intelligent scheduling in multimodal transport, offering innovative solutions for smart port management and integrated transportation systems.

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Optimization of Multi-equipment Intelligent Scheduling in Automated Terminals Integrating Quantum Co-evolution and Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Zekai Wang,
  • Xiaoning Zhu,
  • Li Wang

摘要

As global container transportation grows, the complexity and uncertainty of multi-equipment scheduling in ports demand higher operational efficiency in automated terminals. This paper presents an intelligent scheduling optimization model integrating the Quantum Co-evolutionary Bat Algorithm (QCEBA) and deep learning, focusing on the joint scheduling of quay cranes, AGVs, and yard cranes. The model leverages quantum computing's global search capability and deep learning's dynamic prediction function to enhance multi-equipment collaboration. This approach effectively addresses multi-objective scheduling complexity and provides a theoretical foundation for digital intelligent scheduling in multimodal transport, offering innovative solutions for smart port management and integrated transportation systems.