Research On Speed And Route Optimization Based On Consumption Calculation Under Emission Restriction

With the development of trade globalization,maritime transportation has become the main means of international trade.At present,the port and shipping industry undertakes 90%of the transportation and transit tasks of global trade and is the lifeline of global economic trade and cooperation.With the emergence of environmental problems such as climate warming,the research interest in green shipping has increased significantly.As ships burn marine fuel oil,large amounts of sulfur dioxide,nitrogen oxides and particulate matter are released,causing serious pollution to the atmosphere.The International Maritime Organization(IMO)has established an Emission Control Area(ECA)in order to limit ship emissions,stipulating that navigation in the ECA area requires the use of cleaner but higher-priced low-sulfur oil-MGO.In 2018,the Chinese government also proposed ECA regulations for China’s waters.In addition to China’s coastal ECA within 18 nautical miles of China’s coast,it also stipulates China’s Hainan waters ECA.As fuel costs account for a huge proportion of the total cost of ship operations,the implementation of these ECA policies will inevitably affect the decision-making of ship operators’ navigation plans.In order to strive to maintain their economic goals as much as possible under the premise of compliance,enter and exit the ECA area.The decision-making scheme of ship speed and route has become a hot issue of research.The main factors affecting fuel costs are not only sailing speed and navigation distance,but also by uncertain factors such as wind and ocean currents,such as real weather and ocean currents at sea.Therefore,this article first establishes a deep learning method using ship navigation data and earth weather and hydrological data.The convolutional neural network prediction algorithm for ship fuel consumption can more accurately estimate ship fuel consumption by considering the uncertainty of the atmospheric and sea conditions during navigation.In the second step,this paper constructs a mixed integer optimization model that considers both China’s coastal ECA and China’s Hainan ECA.The objective function of the model is to minimize the total fuel cost of China’s coastal ECA,China’s Hainan waters ECA,and non-ECA regions.In the third step,this article uses the above model and algorithm to quote a real route for numerical experiments,and respectively decides the reasonable sailing plans under the influence of policy changes,seasonal changes,and oil price fluctuations,and evaluates the impact of ECA on ships.The impact of the company and the environment.The research results show that: First,the optimized model can effectively save navigation fuel costs for ship operators when the price of MGO is higher than the price of ULSFO.At the same time,it also explained that the ECA regulations may bring about a series of effects such as shortening of navigation routes in some areas,higher costs,and higher carbon dioxide emissions.The optimized decision-making model tends to choose a route with a shorter voyage in the ECA region,and considering the further tightening of China’s ECA policy,the sulfur emissions of ships have been significantly reduced,which proves the effectiveness of policy formulation.Secondly,this article also considers that with the change of seasons,the total cost of sailing tasks in January will increase to a certain extent compared with sailing tasks in July.Ships will emit more sulfur oxides and carbon dioxide,which will affect ship operations.The quotient has certain practical guiding significance.Finally,this paper also analyzes the impact of international oil price fluctuations on ship operation decisions.The calculation results show that when the price of MGO rises,the price of ULSFO falls,and the ratio of MGO and ULSFO spreads continues to expand,the decision-making optimization cost is compared with the unoptimized cost,which saves utility.Will continue to increase;and when the price of MGO drops and the price of ULSFO rises,and the price of MGO is lower than the price of ULSFO,the model will become invalid,which stipulates the scope of the applicability limit of this model.The speed optimization mixed integer optimization model proposed in this paper is reasonable and effective,and its supporting fuel consumption convolutional neural network prediction algorithm can measure fuel consumption at high speed.It is not only suitable for irregular transportation,but also applicable to any route schemes with time window restrictions that exist in and out of China’s Hainan waters ECA and China’s coastal ECA,such as container liners,and it has certain reference significance.