HPC implements machine learning to predict dwell time

Hamburg Port Consulting (HPC) has announced that it will implement a machine learning solution for predicted dwell time at the Hamburger Hafen und Logistik AG (HHL)’s Container Terminal Burchardkai (CTB).

Based on machine learning technology, the new terminal operation systems (TOS) add-on solution assists to improve container stacking and optimise the pick-up handling.


The term “dwell time” is used to measure the period which a container remains at the terminal covering the interval from its seaside arrival up until leaving the terminal on truck, rail or vessel. So far, for so-called import containers there is no specific information available on the pick-up time by truck upon stack-in slot selection. This can lead to an inefficient container storage location in the yard. This in turn results in a high risk for additional shuffle moves requiring extra resources, maintenance, energy.


To mitigate this operational inefficiency, the joint project bringing together the terminal operator HHLA, the software specialist INFORM and logistics consultant HPC utilizes machine learning technology to predict the individual container dwell time aiming a reduction of container rehandling for import containers at terminals.


“Utilising machine learning and artificial intelligence and integrating these technologies in existing IT infrastructure are the success factors for reaching the next level of optimizations”, commented Jens Hansen, executive board member responsible for IT at HHLA. “A detailed analysis, and a smooth interconnectivity between all different systems enable the value of the improved safety while reducing costs and greenhouse gas emissions.”


“Data availability and data processing is an important key when it comes to utilising AI technology”, added Alexis Pangalos, head of software engineering at HPC. “It requires a detailed domain knowledge of terminal operations to unlock greater productivity of the terminal equipment and connected processes.”

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