HHLA to predict container dwell time with machine learning solutions


Hamburger Hafen und Logistik AG (HHLA) will develop machine learning (ML) solutions to predict the dwell time of a container at a terminal.

In a statement, the terminal operator said it will develop the solutions at its operations at the Port of Hamburg and has already successfully integrated the first two projects into the IT landscape at Container Terminals Altenwerder, CTA and Burcharkai (CTB).

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The projects were driven forward by teams from HHLA and its consulting subsidiary HPC Hamburg Port Consulting.

HPC together with INFORM have integrated the Syncrotess ML Module into the slot allocation algorithms already running within CTB’s terminal control system, ITS.

Explore HPC Hamburg Port Consulting’s solutions for ports and terminals bu visiting its AIS portal

Angela Titzrath, Chairwoman of the Executive Board of HHLA, emphasised the importance of ML for the company in her welcoming address at the World Artificial Intelligence Conference (WAIC) that is taking place in Shanghai from 9 to 11 July.

“Advancing digitalisation is changing the logistics industry and our port business with it,” Titzrath said.

“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, according to HHLA, with the goal being 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 the 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 ML 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.

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.

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