Research On The Optimization Of Ship Speed Based On Improved Genetic Algorithm

In the current sluggish shipping market,how to reduce fuel costs through speed optimization has become an important challenge for the shipping industry.In order to reduce the operating cost,shipping companies have been seeking measures to reduce the fuel consumption of ships to achieve energy saving and emission reduction,and at the same time reduce the operating cost of ships.Therefore,how to optimize the ship speed in long-distance voyage is the key to reduce fuel consumption.In this thesis,we study the segmented speed optimization problem of ocean-going ships on fixed routes and the intelligent algorithm to solve such optimization problem.The study of this problem provides theoretical and technical support to improve the economic efficiency of ship operation of shipping companies.The main work accomplished in the thesis is as follows:1.A ship resistance model is established by analyzing various resistances encountered by ship navigation in wind and waves and their influence on ship navigation.Ship main engine power is calculated according to the relationship between ship main engine speed and ship sailing speed,and the database of ship main engine fuel consumption rate is constructed.Finally,through the calculation of ship resistance and the determination of main engine fuel consumption rate,the ship main engine fuel consumption model under constant speed navigation was established,which laid the theoretical and data foundation for the subsequent segment speed optimization research.2.The segment speed optimization model considering the influence of weather has been established.The model obtains the meteorological data along the target route from the website of the European Center for Medium-Range Weather Forecasts,and uses linear interpolation to obtain the meteorological data of the target ship over a period of time.Based on the time series of the speed loss of the ship in wind and waves,the sea state information within the segment is clustered by using the time clustering theory,and the target route is divided into multiple segments by combining the physical turning points of the ship and the sea state conditions after clustering.The experimental results show that the segmented speed optimization model established in the thesis can minimize the sum of main engine fuel consumption for each segmented route.3.Considering the complexity of the segmented speed optimization model calculation,an intelligent algorithm based on genetic simulated annealing is proposed to solve the model.The adaptation function and various constraints of the algorithm are studied,and the model is solved according to the target ship data and its sailing route.The experimental results show that the total fuel consumption after optimization is 2203.25 t is 61.6t less than that of 2264.85 t calculated by the model before optimization,and the proportion of fuel saved is 2.72%.Therefore,the research results of this paper can be used to optimize the sectional speed of ocean-going ships,provide scientific operation guidance for the crew,help reduce the fuel consumption of the ship’s main engine,and improve the economic efficiency of the ship.