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Predicting and Mitigating Delays in Cross-Dock Operations: A Data-Driven Approach

Author

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  • Amna Altaf

    (UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois, F-62400 Béthune, France
    School of Computer Science and Technology, Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK)

  • Adeel Mehmood

    (School of Computer Science and Technology, Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK)

  • Adnen El Amraoui

    (UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois, F-62400 Béthune, France)

  • François Delmotte

    (UR 3926 Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), University of Artois, F-62400 Béthune, France)

  • Christophe Lecoutre

    (CRIL-CNRS, UMR 8188, University of Artois, F-62307 Lens, France)

Abstract

Cross-docking operations are highly dependent on precise scheduling and timely truck arrivals to ensure streamlined logistics and minimal storage costs. Predicting potential delays in truck arrivals is essential to avoiding disruptions that can propagate throughout the cross-dock facility. This paper investigates the effectiveness of deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLPs), and Recurrent Neural Networks (RNNs), in predicting late arrivals of trucks. Through extensive comparative analysis, we evaluate the performance of each model in terms of prediction accuracy and applicability to real-world cross-docking requirements. The results highlight which models can most accurately predict delays, enabling proactive measures for handling deviations and improving operational efficiency. Our findings support the potential for deep learning models to enhance cross-docking reliability, ultimately contributing to optimized logistics and supply chain resilience.

Suggested Citation

  • Amna Altaf & Adeel Mehmood & Adnen El Amraoui & François Delmotte & Christophe Lecoutre, 2025. "Predicting and Mitigating Delays in Cross-Dock Operations: A Data-Driven Approach," Stats, MDPI, vol. 8(1), pages 1-20, January.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:9-:d:1571368
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    References listed on IDEAS

    as
    1. Gelareh, Shahin & Monemi, Rahimeh Neamatian & Semet, Frédéric & Goncalves, Gilles, 2016. "A branch-and-cut algorithm for the truck dock assignment problem with operational time constraints," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1144-1152.
    2. Mohammad Amin Amani & Mohammad Mahdi Nasiri, 2023. "A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-32, July.
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