Machine Learning in Terminal Operations



Dr Eva Savelsberg, Dr Ulrich Dorndorf, and Matthew Wittemeier, INFORM


This paper analyses how machine learning can develop within the port terminal sphere with specific regard to yard optimization.

Last year we wrote a paper regarding artificial intelligence (AI) on how it had made and was continuing to make its way into the terminal industry. We paid specific attention to how machine learning (ML) as a branch of AI could be implemented. In a paper that likened AI’s current state to that of Frankenstein, the article closed by saying that AI was coming, and that as an industry we can either be prepared or caught off-guard when it does. For INFORM, as a leading AI solution provider, the question wasn’t how to prepare for AI, but rather, how we can leverage the promise of ML and build it into our core AI driven solution.

Read: Demystifying AI – The Human-AI Partnership

As such in 2018, INFORM undertook an ML assessment project looking at maritime container terminals and how ML could be used to improve operational and optimization outcomes. The assessment aimed to achieve two results. Firstly, could INFORM’s broader ML algorithms, developed for use in other industries such as finance, be applied to our Optimization Modules that are used in terminals around the world. Secondly, if they could be, does that mean we can apply them to real-world terminal data and identify areas where improvements could be made to parameters that influence the optimization calculations of INFORM’s add-on Optimization Modules.

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