Journal Article

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

Lang, Maoxiang, Hai, Yuzhilin, Tang, Weilin, Chen, Xinghan, Li, Shiqi, Liu, Yuying

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Algorithms / Cargo handling / Cost allocation / Costs / Efficiency / Equipment and supplies / Heuristic / Heuristic methods / Integer programming / Knapsack problem / Logistics / LTL logistics hub / Matching / Materials handling / Mathematical programming / Optimization / Packaging / Packing problem / palletized transportation / Pallets / Real time / Search algorithms / Standardization / three-dimensional loading / Trucks / truck–cargo matching / two-stage hybrid heuristic / Vehicles / Windows (intervals)