| dc.description.abstract | As a consequence of the global renewable energy transition, there is a rising demand
for transportation of project cargo, such as wind turbine components. Transportation
of this type of cargo requires special considerations as it is sensitive to adverse weather
exposure. This thesis aims to determine what impact weather-sensitive cargo has on
transportation cost, formulated as the expected incremental cost compared to vessels
transporting "regular" cargo. The chosen methodology approach applies a ship weather
routing model to identify the most cost-efficient route from Spain to Houston while
accounting for the required weather considerations. The weather routing model comprises
one of the literature's most prominent pathfinding algorithms combined with complex
machine learning models to achieve realistic cost estimations. Our findings indicate that
vessels carrying weather-sensitive deck cargo have a high tendency to deviate from the
optimal route selected by vessels carrying regular cargo. This is particularly evident in
the winter months, where our findings identify an incremental cost upwards of 13.80%.
Conversely, the results reveal an upper limit on the incremental cost of 0.70% in the
summer months, indicating a relatively modest disparity from the vessels transporting
regular cargo. This asymmetry is found to be largely explained by the seasonal effect of
adverse weather. Our findings suggest tha t vessels transporting weather-sensitive deck
cargo are at a considerably higher risk during the winter months, where exposure to
adverse weather effects is especially prominent. | en_US |
