Research On The Development Forecast And Countermeasures Of Chinese Seafarers Based On The Combinatorial Model

Crew members are an important part of the transportation workforce,bearing the heavy responsibility of transporting 90% of the world’s cargo volume,and are an important human resource for water transportation,which is crucial for ensuring the smoothness of the maritime logistics chain,stabilizing the water transportation safety situation,serving and promoting the construction of a strong transportation country and other major strategies.As of the latest data statistics in 2021,there is a total of 1.774 million registered crew members in China,of which613,000 are unlimited navigation area seafarers and 235,000 are coastal navigation area seafarers.The scale is constantly expanding and the number ranks among the top in the world.However,since the outbreak of the global COVID-19 pandemic in January 2020,travel bans issued by governments in response to the pandemic have had a significant impact on the output of seafarers from countries with severe outbreaks,especially countries with poor pandemic control.The sporadic outbreaks have had a significant impact on the supply and demand pattern of seafarers in China and even globally.At present,the epidemic has been basically brought under control,and the balance between supply and demand of Chinese seafarers needs to be restored to a good situation as soon as possible.Therefore,predicting the future supply and demand situation of Chinese seafarers is crucial for their healthy development.At present,research on the supply and demand problem of Chinese seafarers mainly stays at the theoretical and policy guidance level.A few scholars have established mathematical models to predict the future demand for seafarers,but their research objects are mostly the number of seafarers in a certain province,and they are basically predicted by a single time series prediction model.This thesis takes management-level and operational-level seafarers in China as research objects,and their supply and demand quantity in the next 10 years as research objectives.Firstly,through literature retrieval,data collection and other methods,it deeply understands the current development status of Chinese seafarers team,and explores its influencing factors on supply and demand quantity.On this basis,based on the situation of the pass of Chinese seafarers qualification examinations basic data,it predicts Chinese senior seafarers supply quantity.Based on Chinese marine transport ship quantity and its quantity change factors as basic data,it screens out ship quantity change influencing factors through correlation analysis and regression analysis.It constructs a combination model of secondary exponential smoothing and BP neural network to predict Chinese senior seafarers demand quantity,and compares it with other single time series model methods to demonstrate its superiority.Finally,according to the model prediction results,it calculates that in order to maintain senior seafarers supply-demand balance in 2023-2025(short-term),2025-2028(medium-term),2028-2031(medium-to-longterm)three prediction periods respectively,senior seafarer supply annual growth rate required are 8.45%,6.43% and 6.89% respectively.And according to the prediction of three stages respectively from government agencies,training institutions,shipping enterprises,seafarers themselves,youth maritime education five levels for Chinese seafarers sustainable development gives short-term,medium-term,medium-to-long-term feasible suggestions.At the same time,this thesis compares the predicted results with the predicted growth trend of global seafarers in BIMCO’s “Seafarer Workforce Report 2021” published in 2021.The predicted development trend of this thesis conforms to BIMCO’s prediction growth trend for global seafarers,and the difference between the two calculated results does not exceed 5%,indicating that the predicted results of this article are in line with expectations.In the demand forecasting section,this thesis uses the BP neural network model for the first time to predict the number of Chinese seafarers required.As the years increase,the historical data of the model input layer will continue to expand,and the predicted results will be more accurate.Therefore,the prediction model of this article needs to be further optimized in future research by combining practice.