Browsing by Subject "machian learning"

This thesis aims to offer a practical solution for making cost-effective decisions regarding weather routing deployment to optimize computational costs. The study focuses on developing three collaborative model components that collectively address the challenge of rerouting decision-making. Model 1 involves training a neural network-based Ship Performance Model, which forms the foundation for the weather routing model. Model 2 is centered around constructing a time-dependent path-finding model that integrates real-time weather forecasts. This model optimizes routing within a designated experimental area, generating simulation training samples. Model 3 utilizes the outcomes of Model 2 to train a practical machine learning decision-making model. This model seeks to address the question: should the weather routing system be activated and the route be adjusted based on updated weather forecasts? The integration of these models supports informed maritime decision-making. While these methods represent a preliminary step towards optimizing weather routing deployment frequencies, they hold the potential for enhancing operational efficiency and responsible resource usage in maritime sector.