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dc.contributor.advisorÅdland, Roar Os
dc.contributor.authorKnive, Rikard
dc.contributor.authorLiane, Jon Mæle
dc.date.accessioned2022-11-16T11:11:16Z
dc.date.available2022-11-16T11:11:16Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/11250/3032144
dc.description.abstractAs 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
dc.language.isoengen_US
dc.subjectbusiness analyticsen_US
dc.titleEstimating the Impact of Weather-Sensitive Cargo Risk on Transport Costen_US
dc.typeMaster thesisen_US
dc.description.localcodenhhmasen_US


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