Optimizing Berth Allocation at Lekki Deep Sea Port: A Predictive Model for Efficiency and Growth

Abstract Seaports are essential for global trade, acting as vital hubs within vast freight transport networks. Efficient berth allocation is critical for smooth port operations, minimising vessel wait times, and optimising resource use written. Purpose – This study analyzed berth utilization, vessel service times, traffic seasonality, and revenue at Lekki Deep Seaport. Methodology –This study uses Python-based simulation and data visualisation to analyze berth allocation at Lekki Deep Sea Port, considering factors like vessel arrival rates (averaging one every 2.5 days), service times (1.5 to 2.5 days based on vessel size), berth utilisation under different traffic scenarios, revenue, idle costs, and congestion management via predictive modelling. Findings indicate that the current berth infrastructure is sufficient under present traffic conditions. Findings – Findings indicate that the current berth infrastructure is sufficient under present traffic conditions. However, to prepare for future challenges, proactive measures like optimizing service times and implementing machine learning models are recommended as traffic grows to maintain efficiency. This study offers valuable insights for optimizing port operations and ensuring Lekki Deep Sea Port’s continued contribution to West African economic growth. Originality – Simulation techniques replicate port operations, helping identify bottlenecks and test allocation scenarios