A Ship Route Planning Method under the Sailing Time Constraint

Abstract

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.

Application of fuzzy cellular automata to optimize a vessel route considering the forecasted hydrometeorological conditions

“…The planning of the passage of the ship over long distances with the forecast of weather conditions, unlike that given in [4,11], is represented in time and space in a discrete form. At the same time, the sections of the recommended route bypass any obstacles and clearly ensure the fulfillment of the safety requirements of sailing.…”

“…The simultaneous optimization of the ship's course and speed for the entire transit route within the estimated time of arrival, for safe and energy-efficient navigation, is considered in [4]. The authors propose to build a dynamic model of the sea space with sets of hydrometeorological data extracted from records of previous voyages, which, based on two artificial neural network models, predict the rate of fuel consumption and engine operation mode.…”

A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

“…These results show that algorithm 2 works reliably. When comparing the results with other documents with the same research direction as this paper, typically documents [12], [13], [15], and [17]. These studies all use historical ship operating datasets and lack the influence of current.…”

“…These studies all use historical ship operating datasets and lack the influence of current. Assuming that Scenario 2 takes into account the influence of current (both direction and magnitude), the algorithm finds the optimal fuel route with a combination of the A* algorithm and the neural network (as used in the research [17]) recommends Route 3. However, if the influence of current is ignored, the algorithm will recommend Route 5, the same as Route 1 in Scenario 1.…”

“…The literature [13] uses the multi-objective ant colony algorithm to find the optimal route. Meanwhile, the studies [14]- [17] all use the A* algorithm, in which the research [14] uses a machine learning model from the data of the automatic identification system (AIS) combined with weather data to calculate travel speed, literature [15], [16] combine the A* algorithm and a wave prediction model to find the optimal route. In addition, the work [17] combines A* and a fuel consumption prediction model from the operational data.…”