dc.description.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. | en_US |