This article was published in Edition 83 of Port Technology in 2019.
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 built it into our core AI driven solution.
As such in 2018, INFORM undertook an ML assessment project looking at maritime container terminals and how ML could be used to improve optimization and operational 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.
Working with a randomized sample of 1 million containers handled in the 2017 calendar year with 50 data variables (explanatory variables) at a selected terminal, we set off to answer these two questions. The dataset was split further; using a time slicing method, a training dataset (75% of the dataset) and a testing dataset (25% of the dataset) were created in accordance with good ML practices. Further, we worked with a human export to review and identify variables that amongst the 50 explanatory variables that would prove meaningful in the assessment. We identified 16 variables that have been used to build the random forest ML models.
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