| dc.description.abstract | With the increasing globalization of supply chains, efficient port operations have become
crucial. 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
combining Genetic Algorithms (GA) and Bee Algorithms (BA). Due to Long An
International Port not being fully operational, data from Tan Cang - Cai Mep Thi Vai Port
One Member Company Limited is used to simulate the port's operations. GBA integrates
GA's global search efficiency with BA's local search effectiveness, balancing exploration and
exploitation. Although GBA performs better than BA, the broad search capability of GA has
led to better performance due to the specific characteristics of the problem.
Furthermore, developing a real-time optimization framework for berth allocation is another
promising area for further exploration. This framework would allow the GBA to adapt to
dynamic conditions such as vessel delays or unexpected port congestion. Implementing a
responsive and adaptive version of the GBA can enhance its practical applicability in real world port operations, ensuring optimal berth allocation under varying circumstance. | en_US |
