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Vessel turnaround time prediction: A machine learning approach

  • Published:2024-03 Issue: Volume:249 Page:107021
  • ISSN:0964-5691
  • Container-title:Ocean & Coastal Management
  • language:en
  • Short-container-title:Ocean & Coastal Management

Author:

Chu Zhong,Yan Ran,Wang Shuaian

Funder

Ministry of Education - Singapore

Publisher

Elsevier BV

Reference51 articles.

1. A decision tree model for the prediction of the stay time of ships in Brazilian ports;Abreu;Eng. Appl. Artif. Intell.,2023

2. Machine learning for international freight transportation management: a comprehensive review;Barua;Res. Transp. Bus. Manag.,2020

3. Random forests;Breiman,2001

4. Big data optimization in maritime logistics;Brouer;Big Data Optim. Recent Dev. Chall.,2016

5. XGBoost: extreme gradient boosting, 1 (4);Chen,2015

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1. Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment;Simulation Modelling Practice and Theory;2024-11

2. Predicting vessel service time: A data-driven approach;Advanced Engineering Informatics;2024-10

3. The Role of Applying Artificial Intelligence in Improving Supply Chain Management and Sustainability: Evidence from Egypt Ports;International Journal of Mathematical, Engineering and Management Sciences;2024-10-01

4. Vessel Turnaround Time Reduction in Ports;Advances in Finance, Accounting, and Economics;2024-07-26

5. Leveraging Artificial Intelligence to Enhance Port Operation Efficiency;Polish Maritime Research;2024-06-01
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