This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.
As a result of a global call for energy-saving and emission-reduction strategies as well as an urgent need to reduce the shipping cost of transoceanic crossings, this paper proposes a route that minimizes the time for such crossings and provides technical support
to efficiently utilize wind power based on existing research for wind-assisted ships. To begin, the ocean winds around the ship route were analyzed, and the different influences on traditional ships and wind-assisted ships were listed for various wind speeds and directions. The number of waypoints
of a route was subsequently calculated, and a model of the optimal ship route was then built based on the fixed power output of the main marine engine. A solution algorithm based on simulated annealing was then presented to determine the optimal wind-assisted ship routes by minimizing the
travel time. Finally, a 76,000-DWT wind-assisted cargo ship was designated as the experimental ship, and the optimization model and its algorithm were simulated to generate an optimized wind-assisted route. The simulation indicated that the speed of a ship equipped with wind propulsion increases,
which significantly reduces the travel time and fuel costs over the optimized route, despite the increased distance of this route. Thus, the route optimization algorithm designed in this study can be applied to optimize the routes for wind-assisted ships and theoretically guide further studies
of wind-assisted projects.
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