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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, Adedeji Daniel
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 Daniel
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
Real Time Evidence based Modelling of Gate Congestion of Marine Container Terminals in Nigeria Godwin Nwachukwu Nkem; Gbadebo, Adedeji Daniel
Nama Jurnal Akmi Vol 8 No 1 (2026): J.Sitektransmar May 2026
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v8i1.115

Abstract

Efficient gate operations at marine container terminals (MCTs) are critical for reducing truck waiting times, minimizing congestion, and improving port economic performance. Purpose – This study develops a stochastic queuing-based optimization framework using M/Eₖ/S multi-server models to represent truck arrivals and gate service processes. Statistical goodness-of-fit tests confirmed that truck inter-arrival times follow an exponential distribution, while service times align with an Erlang distribution. Methodology – The methodology adopts a queueing–optimization framework to evaluate and improve marine container terminal (MCT) gate operations by minimizing the combined costs incurred by terminal operators and trucking companies. The gate system is characterized by two principal cost components: gate operating costs borne by the service provider and truck waiting costs incurred by users. Findings – Optimization results indicate that implementing the model reduced average truck waiting times from 9.8 minutes to 6.3 minutes and decreased total daily gate costs by approximately 23% across operating hours. The optimal number of gate lanes varied with traffic density, demonstrating the importance of adaptive lane allocation. Originality – Policy implications highlight investment in predictive analytics, dynamic scheduling, and resource allocation to enhance port efficiency, throughput, and economic competitiveness 
Co-Authors Gbadebo, Adedeji Daniel