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Optimization of Truck–Cargo Matching for the LTL Logistics Hub Based on Three-Dimensional Pallet Loading

Author

Listed:
  • Xinghan Chen

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Weilin Tang

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Yuzhilin Hai

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Maoxiang Lang

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Yuying Liu

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Shiqi Li

    (Collective Intelligence & Collaboration Laboratory, China North Artificial Intelligence & Innovation Research Institute, Beijing 100072, China)

Abstract

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing the three-dimensional loading of goods onto standardized pallets and assigning these loaded pallets to a fleet of heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed to solve this complex logistics challenge. In the first stage, a tree search algorithm based on residual space is developed to determine the optimal layout for the 3D loading of cargo onto pallets. In the second stage, a heuristic online truck–cargo matching algorithm is introduced to allocate loaded pallets to trucks while optimizing the number of trucks used and minimizing transportation costs. The algorithm operates within a rolling time horizon, allowing it to dynamically handle real-time order arrivals and time window constraints. Numerical experiments demonstrate that the proposed method achieves high pallet loading efficiency (close to 90%), near-optimal truck utilization (nearly 95%), and significant cost reductions, making it a practical solution for real-world LTL logistics operations.

Suggested Citation

  • Xinghan Chen & Weilin Tang & Yuzhilin Hai & Maoxiang Lang & Yuying Liu & Shiqi Li, 2024. "Optimization of Truck–Cargo Matching for the LTL Logistics Hub Based on Three-Dimensional Pallet Loading," Mathematics, MDPI, vol. 12(21), pages 1-28, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3336-:d:1505923
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    References listed on IDEAS

    as
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    2. Paquay, Célia & Limbourg, Sabine & Schyns, Michaël, 2018. "A tailored two-phase constructive heuristic for the three-dimensional Multiple Bin Size Bin Packing Problem with transportation constraints," European Journal of Operational Research, Elsevier, vol. 267(1), pages 52-64.
    3. Célia Paquay & Sabine Limbourg & Michaël Schyns & José Fernando Oliveira, 2018. "MIP-based constructive heuristics for the three-dimensional Bin Packing Problem with transportation constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1581-1592, February.
    4. Weilin Tang & Xinghan Chen & Maoxiang Lang & Shiqi Li & Yuying Liu & Wenyu Li, 2024. "Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand," Mathematics, MDPI, vol. 12(5), pages 1-31, March.
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