Optimization of Truck–Cargo Matching for the LTL Logistics Hub Based on Three-Dimensional Pallet Loading
Xinghan Chen,
Weilin Tang,
Yuzhilin Hai,
Maoxiang Lang,
Yuying Liu,
Shiqi Li
Affiliations
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
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.
