A joint venture has seen the implementation of machine learning at HHLAs Container Terminal Burchardkai to optimise import container yard positioning and reduce re-handling moves.

The elimination of costly re-handling moves of import containers has recently been the focus of a joint project between container terminal operator HHLA, its affi liate Hamburg Port Consulting (HPC) and INFORM the Artificial Intelligence (AI) systems supplier. Machine learning sits at the heart of the system.

Dwell time is the unit of time used to measure the period in which a container remains in a container terminal with this typically running from its arrival off a vessel until leaving the terminal via truck, rail or another vessel.

For import containers there is often no specific information available on the pick-up time when selecting a storage slot in the container stack. This can lead to an inefficient container storage location in the yard generating, in turn, the requirement for additional shuffle moves that require extra resources including maintenance and energy consumption.

To mitigate this operational inefficiency, the project partners - HHLA, HPC and INFORM - have recently run a pilot project at HHLAs Container Terminal Burchardkai (CTB) focused on machine learning technology with this applied in order to predict individual import container dwell times and thereby reduce costly re-handling/shuffle moves.

As a specialist in IT software integration and terminal operations, HPC employed the deep learning approach to identify hidden patterns from historical data of container moves at HHLA CTB. This was undertaken over a period of two years and with the acquired information processed 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.

PRODUCTIVE IMPLEMENTATION

Dr. Alexis Pangalos, Partner at HPC discussing the project highlights notes: It was a productive implementation of INFORMs Artificial Intelligence (AI) solution for the choice of container storage positions at CTB. The Machine Learning (ML) Module was trained with data from CTBs container handling operations and the outcome from this is a system tailor-made for HHLAs operations.

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

PREDICTING DWELL TIME

INFORMs AI solution predicts the dwell time (i.e., the time period the container is expected to be stored in the yard) and the outbound mode of transport (e.g., rail, truck, vessel) both of which are crucial criteria for selecting an optimised container storage location within the yard. A location that avoids unnecessary re-handling.

Utilising machine learning and AI and integrating these technologies into existing IT infrastructure are the success factors for reaching the next level of optimisations, says 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 improved safety while reducing costs and greenhouse gas emissions, he underlines.

DETAILED DOMAIN KNOWLEDGE

Data availability and data processing are key elements when it comes to utilising AI technology, says Pangalos. It requires a detailed domain knowledge of terminal operations to unlock greater productivity of the terminal equipment and connected processes.

The implementation is based on a machine learning assessment INFORM undertook in 2018 whereby it set out to determine if they could improve optimisation and operational outcomes using INFORMs broader ML algorithms developed for use in other industries such as finance and aviation.

As of 2019, system results indicated a prediction accuracy of 26% for dwell time predictions and 33% for outbound mode of transport predictions.

Dr. Eva Savelsberg, Senior Vice President of INFORMs Logistic Division notes: AI and machine learning allows us to leverage data from our past performance to inform us about how best to approach our future operations our ML Module gives our Operations Research based algorithms the best footing for making complex decisions about what to do in the future.

INFORMs Machine Learning Module allows CTB to leverage insights generated from algorithms that continuously learn from historical data."

Further Information: Matthew Wittemeier m.wittemeier@inform-software.com

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PREDICTING THE OPTIMUM PATH - Port Strategy