"Machine learning solutions provide us with many opportunities to increase productivity and capacity rates at the terminals," said HHLA chairwoman Angela Titzrath.

The first two projects have commenced at Container Terminals Altenwerder (CTA) and Burchardkai (CTB), she told the World Artificial Intelligence Conference (WAIC) in Shanghai.

When a container is stored in the yard, its pickup time is frequently unknown, said the HHLA statement. "In future, the computer will calculate the probable container dwell time, using an algorithm based on historic data which continually optimises itself using state-of-the-art machine learning methods," it said.

"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," HHLA said.

"A significant 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," said the HHLA statement.

https://www.shippingazette.com/menu.asp?encode=eng