Ship speed optimization method combining Fisher optimal segmentation principle

Abstract

An iterative optimization method that combines the principle of ordered clustering was proposed to address the strong correlation among route segmentation, weather loading, and ship speed optimization. Focusing on a single voyage task on a given route, the speed of an ocean-going container ship was optimized to achieve the minimum fuel consumption of the ship. The verified data-driven method was used to build a ship fuel consumption prediction model, and the Fisher optimal segmentation method was applied to segment ship routes. Subsequently, the speed optimization problem was solved using an iterative optimization method. The optimization results confirmed that the iterative optimization method has significant advantages in realizing route segmentation with the highest weather similarity within the segment. Extensive empirical studies on six case routes revealed the significant fuel-saving potential of the proposed iterative optimization method. Under different routes and weather conditions, the proposed iterative optimization method achieves a ship fuel-saving rate of 2.4 %–5.4 % and provides a reliable technical reference for the shipping industry to reduce the energy consumption and emissions of ships.