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Machine Learning and Simulation for Efficiency and Sustainability in Container Terminals

Author

Listed:
  • Abderaouf Benghalia

    (Department of Computer Science, Faculty of Sciences, University of Algiers, Algiers 16000, Algeria)

  • Amani Ferdjallah

    (Department of Computer Science, Faculty of Sciences, University of Algiers, Algiers 16000, Algeria)

  • Mustapha Oudani

    (TICLab, College of Engineering and Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Jaouad Boukachour

    (IUT, Le Havre Normandy University, 76610 Le Havre, France)

Abstract

This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine learning algorithms with discrete-event simulation to predict ship turnaround times using historical data. The proposed approach is empirically validated with data from the Algiers Port Container Terminal, achieving an exceptionally high predictive precision of 0.9991. Simulating terminal operations with both real and predicted data offers valuable insights into improving performance. The results demonstrate that minimizing empty trips and reducing the waiting times for handling equipment significantly enhance turnaround time. Additionally, optimizing terminal operations reduces carbon emissions, aligning with sustainable development objectives in port logistics. This study proposes a novel integration of machine learning and simulation, demonstrating its effectiveness in optimizing ship turnaround times and reducing carbon emissions. By integrating machine learning and discrete-event simulation, this research offers new perspectives on port logistics, contributing to the advancement of sustainable and efficient terminal operations.

Suggested Citation

  • Abderaouf Benghalia & Amani Ferdjallah & Mustapha Oudani & Jaouad Boukachour, 2025. "Machine Learning and Simulation for Efficiency and Sustainability in Container Terminals," Sustainability, MDPI, vol. 17(7), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:2927-:d:1620621
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    References listed on IDEAS

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    1. Lokuge, Prasanna & Alahakoon, Damminda, 2007. "Improving the adaptability in automated vessel scheduling in container ports using intelligent software agents," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1985-2015, March.
    2. Kim, Kap Hwan & Lee, Keung Mo & Hwang, Hark, 2003. "Sequencing delivery and receiving operations for yard cranes in port container terminals," International Journal of Production Economics, Elsevier, vol. 84(3), pages 283-292, June.
    3. Fotuhi, Fateme & Huynh, Nathan & Vidal, Jose M. & Xie, Yuanchang, 2013. "Modeling yard crane operators as reinforcement learning agents," Research in Transportation Economics, Elsevier, vol. 42(1), pages 3-12.
    4. Bakar, Nur Najihah Abu & Bazmohammadi, Najmeh & Çimen, Halil & Uyanik, Tayfun & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Data-driven ship berthing forecasting for cold ironing in maritime transportation," Applied Energy, Elsevier, vol. 326(C).
    5. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    6. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
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    Cited by:

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