Research on ship route and speed optimization considering rolling meteorological data: intelligent decision design based on genetic algorithm

Abstract

Ship route and speed optimization has become an important research area in the maritime industry, aiming to minimize fuel consumption and reduce operational costs. The method proposed in this study is based on rolling meteorological data and genetic algorithms, and includes the following steps. First, historical route data is used to train the model to obtain the relationship between fuel consumption and meteorological conditions and ship speed. Then, predicted meteorological data is obtained, including wind, waves, swells, currents, and their velocities, periods, and directions. Genetic algorithms are used to optimize ship routes and speeds based on these data. Finally, due to insufficient meteorological data to support full-route optimization, when new meteorological forecasts become available, rolling meteorological data and the ship's current position are used for the next optimization until the route is completed. The results show that intelligent decision design based on genetic algorithms significantly reduces fuel consumption compared to traditional methods of finding the shortest path and maintaining a constant speed. The reduction exceeds 3.5% while keeping the sailing time unchanged, and the decision-making time is less than ten minutes, verifying the practicality of the method used in this paper.