Yard planning is one of the important factors affecting the port operation efficiency. In the scenario of automated container terminal, the self-driving container truck fleet is scheduled by the autonomous driving server, which puts forward higher requirements for the balance of the yard plan. According to the needs of engineering and practice, an integer programming model for restoring engineering scenarios is established and solved. This model takes the workload balance inside the block and between blocks and some other indicators as the optimization objective, and considers conventional constraints and the influence of containers delivering or picking-up on the self-driving truck operation. The cost comparison between the model optimization solution and the manual plan proves that the model can effectively reduce the workload imbalance of the yard, avoid the conflict of containers delivering or picking up and improve the yard utilization rate, and it can also work well in scenario with large volume of containers. In the production environment of Rizhao Port, the engineered code replaces the manual plan with the auto-generated yard plan, improves the efficiency and rationality of the yard plan, and has practical significance for improving the efficiency of self-driving truck horizontal transportation in fully automated container terminals.
Research on Workload Balance-Oriented Method of Yard Planning for Fully Automated Container Terminal
Lect. Notes Electrical Eng.
International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024
The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation ; Kapitel : 2 ; 12-31
19.04.2025
20 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
fully automated container terminal , yard planning , workload balance , integer programming , self-driving container truck Engineering , Control, Robotics, Mechatronics , Computational Intelligence , Mechanical Engineering , Artificial Intelligence , Transportation Technology and Traffic Engineering
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