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