Predictive Analysis for Optimizing Port Operations

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 2877 - 2877

Published: March 7, 2025

Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, intricate planning involved in this often hindered by uncertainties, including weather conditions, cargo diversity, port dynamics, leading to increased costs. Consequently, accurate estimation total (stay) time vessel any delays at are essential efficient scheduling operations. This study aims develop predictive analytics address shortcomings previous works operations vessel’s Stay Time Delay Time, offering valuable contribution field maritime logistics. The proposed solution designed assist decision-making environments predict service delays. demonstrated through case on Brazil’s ports, where best performance observed tree-based methods. Additionally, feature analysis used understand interpret key factors impacting logistics, enhancing overall understanding complexities

Language: Английский

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Leveraging Artificial Intelligence to Enhance Port Operation Efficiency

Polish Maritime Research, Journal Year: 2024, Volume and Issue: 31(2), P. 140 - 155

Published: June 1, 2024

Abstract Maritime transport forms the backbone of international logistics, as it allows for transfer bulk and long-haul products. The sophisticated planning required this form transportation frequently involves challenges such unpredictable weather, diverse types cargo kinds, changes in port conditions, all which can raise operational expenses. As a result, accurate projection ship’s total time spent port, anticipation potential delays, have become critical effective activity management. In work, we aim to develop management system based on enhanced prediction classification algorithms that are capable precisely forecasting lengths ship stays delays. On both training testing datasets, XGBoost model was found consistently outperform alternative approaches terms RMSE, MAE, R2 values turnaround waiting period models. When used model, had lowest RMSE 1.29 during 0.5019 testing, also achieved MAE 0.802 0.391 testing. It highest 0.9788 0.9933 Similarly, outperformed random forest decision tree models, with greatest phases.

Language: Английский

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Forecasting train travel times of China–Europe Railway Express through a hybrid deep learning model optimized with a bandit-based approach

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 150, P. 110552 - 110552

Published: March 21, 2025

Language: Английский

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A Data-Driven framework for predicting ship berthing time and optimizing port operations at Tan Cang Cat Lai Port, Vietnam

Case Studies on Transport Policy, Journal Year: 2025, Volume and Issue: unknown, P. 101441 - 101441

Published: April 1, 2025

Language: Английский

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High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach

Maritime Transport Research, Journal Year: 2025, Volume and Issue: 8, P. 100133 - 100133

Published: April 26, 2025

Language: Английский

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Automatic identification system data to assess container port performances

Transportation research procedia, Journal Year: 2025, Volume and Issue: 86, P. 612 - 619

Published: Jan. 1, 2025

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Vessel arrival time to port prediction via a stacked ensemble approach: Fusing port call records and AIS data

Transportation Research Part C Emerging Technologies, Journal Year: 2025, Volume and Issue: 176, P. 105128 - 105128

Published: May 16, 2025

Language: Английский

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Predicting vessel service time: A data-driven approach

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102718 - 102718

Published: July 30, 2024

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Predicting vessel arrival times on inland waterways: A tree-based stacking approach

Ocean Engineering, Journal Year: 2024, Volume and Issue: 294, P. 116838 - 116838

Published: Jan. 24, 2024

Language: Английский

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Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment

Simulation Modelling Practice and Theory, Journal Year: 2024, Volume and Issue: 136, P. 103011 - 103011

Published: Aug. 17, 2024

Language: Английский

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