HHLA uses machine learning to predict dwell time at Hamburg container terminals
July 13, 2020: Hamburger Hafen und Logistik AG (HHLA) announced that it is one of the first ports worldwide to use machine learning (ML) to predict the dwell time of containers by installing them at the Hamburg container terminals.
A positive effect can already be seen at both terminals since the containers are stored based on their predicted pickup time and must, therefore, be moved less frequently.
July 13, 2020: Hamburger Hafen und Logistik AG (HHLA) announced that it is one of the first ports worldwide to use machine learning (ML) to predict the dwell time of containers by installing them at the Hamburg container terminals. The first two projects have now been integrated and implemented into the IT landscape at Container Terminals Altenwerder (CTA) and Burchardkai (CTB).
A positive effect can already be seen at both terminals since the containers are stored based on their predicted pickup time and must, therefore, be moved less frequently. The projects were driven forward by teams from HHLA and its consulting subsidiary HPC Hamburg Port Consulting.
Angela Titzrath, chairwoman, executive board, HHLA, said, “Advancing digitalisation is changing the logistics industry and our port business with it. Machine learning solutions provide us with many opportunities to increase productivity and capacity rates at the terminals.”
The productivity of automated block storage at CTA will be increased by means of an ML-based forecast. The goal is to predict the precise pickup time of a container. Processes are substantially optimised when a steel box does not need to be unnecessarily restacked during its dwell time in the yard. When a container is stored in the yard, its pickup time is frequently still unknown. In future, the computer will calculate the probable container dwell time. It uses an algorithm based on historic data which continually optimises itself using state-of-the-art machine learning methods.
A similar solution is applied at the CTB, where a conventional container yard is used alongside an automated one. Here too, ML supports terminal steerage by allocating optimised container slots. In addition to the dwell time, the algorithm can help calculate the type of delivery. The machine learning solutions can predict whether a container will be loaded onto a truck, the train, or a ship much more accurately than can be determined from the reported data.
