Multi-objective Speed Optimization Of Ship Routing Based On Weight Method And Genetic Algorithm

Ships are today’s major fuel consumers and pollutant emitters.Relevant studies have shown that reducing ship speed can reduce fuel consumption to a certain extent,but the reduction in speed will come at the cost of increasing sailing time.For a shipping company,it is more concerned about how to make profits.Prolonged sailing time will inevitably increase the cost of chartering and maintenance of the ship,so pollutant emissions and the economy of the ship are contradictory.At the same time,considering the economic and environmental benefits of ships is of great significance to optimize the speed of ships.Taking the 48000 DWT oil tanker as the research object,the static water resistance model,wind and wave additional resistance model,propulsion system model,main engine and auxiliary engine fuel consumption model were established,and the static water was verified by the 48000 DWT tanker tank test and the "W" ore ship Resistance and wind and waves add resistance.On the basis of the fuel consumption model,a multi-objective optimization model of ship speed and a calculation model of weight coefficients that simultaneously consider carbon dioxide emissions and operating costs are established.The source program for multi-objective optimization of ship speed was developed using Python language.The weight method and genetic algorithm are used to calculate the Pareto optimal solution and the best compromise solution of the two goals,and then the multi-objective speed optimization results are analyzed and compared with the single-objective optimization method.The research results show that the error between the static water resistance model calculation results and the tank test results is within 4%,and the wind and wave additional resistance model calculation results are consistent with the "W" trend.The weight coefficients of the two targets are different in different shipping environments.When the oil price is higher,the weight coefficient of emissions is larger,and when the ship rent is higher,the weight coefficient of the operating cost is larger.When the fuel cost is high and the rent is low,the result of multi-objective optimization is almost the same as that of single-objective optimization.In other cases,multi-objective optimization of speed is more advantageous than single-objective optimization.At high rents and low fuel prices,compared with the lowest operating cost,the weighted method optimizes speed operation costs by 5.6% and reduces carbon emissions by 16.3%,and genetic algorithm optimizes speed by 9.1% and reduces carbon emissions by 20.7%;and carbon emissions Compared with the lowest speed,the weighted method optimized speed operation cost reduced by 82,000 US dollars,the navigation time decreased by 56.1 hours,the carbon emissions increased by 280.2 tons,the genetic algorithm speed carbon emissions increased by 344.14 tons,the operating cost decreased by 108,400 US dollars,and the navigation time decreased by 52.3.hour.The speed of multi-objective optimization is the best speed after weighing the two goals.