Container dwell time prediction solution
A solution for predicted container dwell time will be implemented at a German container terminal to improve efficiency.
Hamburg Port Consulting (HPC) plans to implement the new terminal operation system (TOS) add-on solution for predicted container dwell time at HHLA’s Container Terminal Burchardkai (CTB) at the Port of Hamburg. Based on machine-learning technology, the 'Dwell Time Prediction' solution is designed to improve container stacking and optimise pick-up handling.
“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," said Jens Hansen, HHLA executive board member, responsible for IT. “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.”
HHLA, HPC and software specialist INFORM have been working together to utilise machine learning technology to predict the individual container dwell time, aiming for a reduction of container rehandling for import containers at terminals.
HPC identified hidden patterns from historical data of container moves at HHLA CTB over a period of two years and processed this information into high quality data sets. Assessed by the Syncrotess Machine Learning Module from INFORM and validated by the HPC simulation tool, the results show a significant reduction of shuffle moves resulting in a reduced truck turn time.
The integration into the slot allocation of the existing TOS system, Integrated Terminal Control System (ITS), ensures the usability of the Dwell Time Prediction solution. The algorithm works in the background and further optimises its prediction, based on the running operational data.
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