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With the shipping market still sluggish, the shipping industry has to pay attention to energy saving and emission reduction. In order to reduce the operating costs of ships, many shipping companies have opted for a reduced speed sailing strategy to reduce the consumption of main engine fuel, thus achieving energy savings and reducing the operating costs of ships. Therefore, selecting the best speed for a ship is the key to achieving speed reduction. In this paper, we study the segmental speed optimisation of ocean-going vessels and its intelligent algorithm, which provides the theoretical basis and technical support for shipping companies to improve the economy of ship operation. First, the meteorological data of the target ship over time was obtained by obtaining the meteorological information of the target route from the European Centre for Medium-Range Weather Forecasts and performing linear interpolation. Then, based on the theory of temporal clustering, segmental clustering of the ship's stall speed values in wind and waves was performed, and the target route was divided into 23 segments by combining the physical turning points of the ship. A segmental speed optimisation model is developed in this paper with the aim of minimising the fuel consumption of the main engine during the voyage. Finally, an intelligent segmental speed optimisation algorithm based on a genetic simulated annealing algorithm is proposed and the model is solved. The segmental speed optimisation model is solved and simulated according to the target route of the target ship selected in this paper. The simulation results show that the fuel consumption of the main engine of the voyage is reduced by 49.4t, which is 2.25% of the total consumption before optimisation, when the optimal segmental speed strategy is adopted.
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