A Study on Container Storage Optimization in Yards Based on a Hyper-Heuristic Algorithm with a Q-Learning Mechanism - Chen, Lifen; Lin, Jiajun; Xu, Shihao (International journal of computational intelligence systems, 2025)
In the context of a low-carbon economy, scientific methods to reduce carbon emissions have become an important issue for many ports. Carbon emissions in port areas mainly arise from vessels and handling equipment. Therefore, an effective resource assignment and equipment arrangement system could not only reduce carbon emissions, but also improve the port’s operational efficiency. This study considers factors such as the arrival order of container trailers, the cargo weight, and the number of container rehandling operations. The objective is to minimize the carbon emissions and the number of container rehandling operations in ports, for which a mixed-integer linear programming model is built. Both heuristic algorithms and hyper-heuristic algorithms are employed to optimize the container storage plan, and their applicability in storage optimization is compared. The results indicate that hyper-heuristic algorithms outperform heuristic algorithms in terms of solution quality and stability, effectively satisfying the storage requirements of the yard while minimizing the carbon emissions and the number of container rehandling operations. The results provide theoretical support for port enterprises in improving their operational efficiency and achieving their goals regarding low carbon emissions.