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With globalization expanding, efficient port operations have become crucial in logistics. This thesis addresses the Berth Allocation Problem (BAP) at Long An International Port, aiming to optimize berth assignments for incoming vessels. The study focuses on dynamic vessel arrivals and discrete berth layouts, proposing a hybrid optimization approach that combines Genetic Algorithms (GA) and Bee Algorithms (BA). Due to Long An International Port not being fully operational, data from Tan Cang-Cai MepThi Vai Port is used to simulate operations. The hybrid GA-BA approach leverages GA’s global search efficiency and BA’s local search effectiveness, balancing exploration, and exploitation. Results indicate that GA-BA performs better than standalone BA, with GA’s broad search capabilities providing superior performance. Future work includes developing a real-time optimization framework to adapt to dynamic conditions such as vessel delays or unexpected port congestion. Implementing an adaptive GA-BA can enhance practical applicability, ensuring optimal berth allocation under varying circumstances.

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Hybrid Genetic Bee Algorithm (GBA) for Berth Allocation Problem in a Case Study of Long An International Port

  • 会议论文

  • 发表日期：

    2025年

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  • Tran Duc Vi,
  • Truong Minh Duc

摘要

With globalization expanding, efficient port operations have become crucial in logistics. This thesis addresses the Berth Allocation Problem (BAP) at Long An International Port, aiming to optimize berth assignments for incoming vessels. The study focuses on dynamic vessel arrivals and discrete berth layouts, proposing a hybrid optimization approach that combines Genetic Algorithms (GA) and Bee Algorithms (BA). Due to Long An International Port not being fully operational, data from Tan Cang-Cai MepThi Vai Port is used to simulate operations. The hybrid GA-BA approach leverages GA’s global search efficiency and BA’s local search effectiveness, balancing exploration, and exploitation. Results indicate that GA-BA performs better than standalone BA, with GA’s broad search capabilities providing superior performance. Future work includes developing a real-time optimization framework to adapt to dynamic conditions such as vessel delays or unexpected port congestion. Implementing an adaptive GA-BA can enhance practical applicability, ensuring optimal berth allocation under varying circumstances.