ML2Grow is deploying machine learning at the Port of Antwerp

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What have we achieved:

  • Predictive machine learning models that capture planning uncertainties

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Added value:

  • Captures the planning horizon for up to 8 hours
  • Avoid expensive capacity shortages
  • Reliable simulations of personnel assignments and alternative scenarios

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haven van antwerpen, ontladen van schepen

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The port of Antwerp is a world in itself. Very few outsiders know what really happens in Europe’s second largest port. Everyone knows the classic image of ships coming and going to load and unload goods. To make this possible, numerous players are needed, each of whom forms an indispensable link in the nautical chain in the port area: from lockmen, bridge keepers, tug crews and ship coordinators to pilots and boatmen. The safe piloting, mooring and unmooring of ships is the core business of Brabo.

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Brabo provides training for port pilots and pilot services for the control of incoming and outgoing ships in the port of Antwerp. In order to be able to cope with the increasing demand and always have sufficient capacity without unnecessary (high wage) costs, Brabo decided to invest in technology that predicts demand, enables better planning and avoids a capacity shortage in view of the associated high (private and social) costs. ML2Grow was able to offer them this technology.

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Brabo is in need of such a system, making sure that even in peak times Brabo pilots are able to provide the services needed. The model will allow dispatchers to manage the available quota pilots as well as extra resources at their disposal.

CEO Ronny Detienne, Brabo Group

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Answering these questions was no easy task, the pilotage time of ships must be predicted on the basis of a large complexity of parameters: type of ship, size of the ship, draft, place of departure and destination, equipment (stern thruster, bow thruster), tugs, …

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ML2Grow was able to successfully make a predictive machine learning system operational, enabling the port to react more quickly with their ship operations. Thanks to these models, Brabo’s pilot service window could be improved from 20 minutes to 8 hours, making a huge difference in Europe’s second largest port.

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Brabo is one of our best examples on how the introduction of a small AI system can open the door to many opportunities. Quick wins in AI are possible with a minimum of investment.

Joeri Ruyssinck, CEO ML2Grow

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This was a textbook example of the kind of problem ML2Grow is eager to solve: applying advanced machine learning for business challenges within a global context. The rapid emergence of artificial intelligence offers unseen possibilities to build new applications that add clear and immediate value to organizations. Building on solid academic foundations and driven by strong ambitions to impact the world, ML2Grow is operating at the core of this ongoing evolution.

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Our model accurately predicts an impending shortage of pilots six hours ahead.


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