Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia

Abstract

Designating the ideal shipping route can spare expenses, enlarge profits and improve the competitiveness of shipping companies. Liner shipping route choice is mainly contingent on fuel cost, which always contributes the major proportion of the ship's operating cost. Although many studies on this topic have been carried out, none are based on the fuel consumption forecast model designed by the advanced machine learning method. This paper provides a platform idea for selecting the optimal operating route for container ships to minimize fuel cost by using an asymmetric traveling salesman problem (ATSP) algorithm solution, in which the fuel consumption model for the route is estimated based on the deep-machine learning method. Five input variables are given in the model including average velocity, sailing time, ship's capacity, wind speed, and wind direction. The mean absolute percentage error (MAPE) of the model is 5.89%, indicating that the predictive result obtains a very high accuracy, close to 95%. The optimal model is thus applied in combination with ATSP to address the optimal solution for a certain route.

Keywords

• Asymmetric traveling salesman problem
• Ship fuel consumption model
• Liner shipping
• Route choice optimization
• Deep-learning neural network
• Container ships