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Machine learning as a decision support system in capesize route optimization : predicting optimal route selection using recurrent neural networks and extreme gradient boosting

Bomholt, Herman Johan; Thune, Torsten Stangeland
Master thesis
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URI
https://hdl.handle.net/11250/2682422
Date
2020
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  • Master Thesis [3749]
Abstract
In this thesis, we investigate the applicability of machine learning to predict optimal route

decisions for Capesize vessels. Our approach uses windows of historical macroeconomic

and market-specific variables to form a time-series classification problem. We fit a

Long-Short-Term-Memory neural network and an Extreme Gradient Boosting model on

historical optimal trip choices and predict an out-of-sample routing strategy. By relying

on historical input and no knowledge of the future, we can compute possible economic

gains, and evaluate the viability of machine learning as a decision support system in route

optimization.

In a simplified scenario, with two geographically different roundtrips, we evaluate

cumulative earnings throughout a three-year period. Our findings suggest the machine

learning methods can outperform an approximation of the average earnings of market

participants by almost 11%. Our thesis contributes to the existing literature on spatial

efficiency and routing optimization in the dry bulk market and provides insights into a

possible method of using machine learning in out-of-sample route prediction.

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