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.
As a specialist in IT software integration and terminal operations, HPC uses the deep learning approach to identify 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.
“Utilizing machine learning and artificial intelligence and integrating these technologies in existing IT infrastructure are the success factors for reaching the next level of optimizations”, says Jens Hansen, as 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”, says 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.”
The integration into the slot allocation of the existing TOS system, Integrated Terminal Control System (ITS), ensures its user-friendly usability. The algorithm works in the background and further optimises its prediction, based on the running operational data.
“INFORM’s Machine Learning Module allows CTB to leverage insights generated from algorithms that continuously learn from historical data,” says Dr. Eva Savelsberg, Senior Vice President of INFORM’s Logistic Division.
The TOS add-on solution Dwell Time Prediction is terminal-specific and can be adopted to other terminals as well.
For more information about HPC’s IT consulting and latest updates on solutions for ports, marine terminals and intermodal rail, please visit www.hpc-hamburg.de.
