Loading optimization of container semi-trailer trucks considering load stability

ObjectiveLoad stability is a critical factor for ensuring vehicle driving safety. This study focuses on container semi-trailer trucks and aims to investigate the container loading optimization that balances load stability and load efficiency.MethodFirst, a multi-container loading problem considering load stability was proposed and decomposed into two sub-models, i.e., a wall-unit sub-model aimed at maximizing space utilization, and a wall-unit assignment sub-model designed to minimize both the number of vehicles used and the deviation of center of gravity. Second, a hybrid heuristic algorithm, based on wall-building approach and improved genetic algorithm (HHA-WBIGA), was developed for two-stage solution. In the first stage, a wall-building approach was employed to combine the cargo to be loaded into a set of candidate wall-unit. In the second stage, an improved genetic algorithm was designed to load these wall units onto vehicles. By adjusting the positions of wall units within each vehicle, the load efficiency was maximized while satisfying the load stability requirements. Finally, the effectiveness of the proposed model and algorithm was validated through computational experiments using benchmark instance from the literature and real-world enterprise case.ResultCompared with the benchmark algorithm, HHA-WBIGA reduces the longitudinal deviation of center of gravity by 15.11%, thereby achieving better load stability. Compared with the enterprise's current loading scheme, HHA-WBIGA loads an average of 1 067.5 kg of more cargo per vehicle. It reduces the average longitudinal deviation of center of gravity by 38.58%, increases the average vehicle volume utilization rate by 1.21%, and improves the average vehicle weight utilization rate by 3.15%.ConclusionThe findings achieve high vehicle utilization while ensuring load stability, providing a methodological reference for solving the loading optimization problem of container semi-trailer trucks considering load stability.