Machine learning system implemented at Hamburg terminals to predict container dwell time

Hamburger Hafen und Logistik (HHLA) reports that it has successfully completed machine learning implementation projects at two of its Hamburg container terminals, Altenwerder and Burchardkai, to improve prediction of dwell times for containers at the facilities. 

The goal of the machine learning system is to more accurately predict the precise pickup time of a container, allowing processes to be optimised as the boxes do not need to be unnecessarily restacked during their dwell time in the yard. 

HHLA notes that when a container is stored in the yard, its pickup time is frequently still unknown. With the new system, the computer will calculate the probable container dwell time using an algorithm based on historical data, which continually optimises itself using machine learning methods. 

“Advancing digitalisation is changing the logistics industry and our port business with it,” said Angela Titzrath, Chairwoman of the Executive Board of HHLA. 

“Machine learning solutions provide us with many opportunities to increase productivity and capacity rates at the terminals.” 

At the Burchardkai terminal, where a conventional container yard operates alongside an automated facility, machine learning is being used to support terminal steerage by allocating optimised container slots. 

In addition to the dwell time, HHLA says that the algorithm can help calculate the type of delivery, predicting whether a container will be loaded onto a truck, train, or a ship more accurately than can be determined from the reported data. 

Share this story

Share on facebook
Share on twitter
Share on linkedin
Share on whatsapp
Share on print
Share on email

About the Author

Rob O'Dwyer
Rob O'Dwyer

Rob is Chief Network Officer and one of the founders of Smart Maritime Network. He has worked in the maritime technology sector since 2005, managing editorial for a range of leading publications in the transport and logistics sector. Get in touch by email by clicking here, or on LinkedIn by clicking here.

Further Reading

News Archive