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All Journal Jurnal Sains Teknologi Transportasi Maritim
Godwin Nwachukwu Nkem
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Decision Sciences, Operations Research & Management Energy Transportation

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Optimizing Berth Allocation at Lekki Deep Sea Port: A Predictive Model for Efficiency and Growth Godwin Nwachukwu Nkem; Gbadebo, Mr
Nama Jurnal Akmi Vol 7 No 2 (2025): Jurnal Sitektransmar November 2025
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v7i2.112

Abstract

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
Predicting Container Delivery Dates Using Machine Learning Techniques: A Regression Approach Godwin Nwachukwu Nkem; Gbadebo, Adedeji
Nama Jurnal Akmi Vol 7 No 2 (2025): Jurnal Sitektransmar November 2025
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v7i2.113

Abstract

Abstract Inland container delivery constitutes a critical component of the global maritime logistics chain, acting as the final phase that connects international ports to inland destinations. Accurate prediction of inland container delivery times is crucial for enhancing operational efficiency, minimizing demurrage and detention costs, and improving customer satisfaction across global supply chains. Purpose –. This study leverages historical container movement data across key international ports to develop a robust machine learning model for predicting inland container delivery timelines. Methodology –. Using a Random Forest Regressor, the model was trained to forecast the total inland delivery time based on features such as container size, type, shipping line, dispatch weekday, and temporal patterns. Findings – The findings have practical implications for shipping lines, freight forwarders, port authorities, and inland terminal operators seeking to optimize logistics planning, reduce uncertainty, and improve supply chain. Evaluation of the model's performance yielded a Mean Absolute Error of 4.59 days, a Root Mean Squared Error of 10.55 days, and a coefficient of determination of 0.68, indicating moderate predictive accuracy. Supporting visualizations - including learning curves, gain curves, feature importance plots, residual distributions, and prediction bands - illustrate the model's strengths and areas for further refinement. Originality – The study contributes to the growing field of intelligent logistics and maritime informatics by providing a data-driven framework for improving inland delivery predictability
Co-Authors Gbadebo, Adedeji Gbadebo, Mr