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Efficient Hub-Based Platooning Management Considering the Uncertainty of Information

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  • Young Kwan Ko

    (Department of Hotel and Tourism Management, College of Hospitality and Tourism, Sejong University, 209 Neungdong-ro, Seoul 05006, Republic of Korea)

  • Young Dae Ko

    (Department of Hotel and Tourism Management, College of Hospitality and Tourism, Sejong University, 209 Neungdong-ro, Seoul 05006, Republic of Korea)

Abstract

Platooning technology, which reduces fuel consumption by decreasing aerodynamic drag, is emerging as a key solution for enhancing road efficiency and environmental sustainability in logistics. Conventional vehicle-to-vehicle communication has limitations when forming platoons across multiple trucking companies. To overcome these limitations, a hub-based platooning system has been proposed, enabling coordinated vehicle platoons through hubs distributed along highways. This study develops a mathematical model to optimize platoon formation at hubs, considering the reality that uncertainty in vehicle arrival times can be resolved as vehicles approach the hub and use vehicle-to-hub communication. The model applies robust optimization techniques to consider worst-case vehicle arrival scenarios and examine how the range of data exchange points—where exact arrival times become known—affects platoon efficiency. Numerical experiments demonstrate that if the range of data exchange points is sufficiently wide, optimal efficiency can be achieved even under uncertainty. Sensitivity analysis also confirms that reducing uncertainty enhances energy savings efficiency. This study provides practical insights into forming vehicle platoons in uncertain environments, contributing to the economic and environmental benefits of the logistics industry. Future studies could extend the model to multiple hubs and consider stochastic disruptions, such as communication failures.

Suggested Citation

  • Young Kwan Ko & Young Dae Ko, 2024. "Efficient Hub-Based Platooning Management Considering the Uncertainty of Information," Mathematics, MDPI, vol. 12(23), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3841-:d:1537435
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    References listed on IDEAS

    as
    1. Wei Liu & Zhongyang Wei & Yuchen Liu & Zhenyu Gao, 2024. "Adaptive Fixed-Time Safety Concurrent Control of Vehicular Platoons with Time-Varying Actuator Faults under Distance Constraints," Mathematics, MDPI, vol. 12(16), pages 1-17, August.
    2. Noruzoliaee, Mohamadhossein & Zou, Bo & Zhou, Yan (Joann), 2021. "Truck platooning in the U.S. national road network: A system-level modeling approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. Anca Maxim & Ovidiu Pauca & Romeo-Gabriel Amariei & Florin-Catalin Braescu & Constantin-Florin Caruntu, 2023. "Coalitional Control Strategy for a Heterogeneous Platoon Application," Mathematics, MDPI, vol. 12(1), pages 1-22, December.
    4. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    5. Larsen, Rune & Rich, Jeppe & Rasmussen, Thomas Kjær, 2019. "Hub-based truck platooning: Potentials and profitability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 249-264.
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