基于BP神经网络的集装箱船舶配载问题研究

针对集装箱船舶对配载的快速性、高效性需求,根据BP神经网络对历史数据的分析,结合集装箱船舶的特性深入研究集装箱智能配载方案.基于集装箱船配载图和各卸货港信息等历史数据,通过BP神经网络对历史数据进行训练优化,挖掘其中规律,从而快速生成对各类集装箱合适的配载位置,优化装船作业,提高集装箱船舶配载效率,达到节约成本的效果.最后采用实船数据进行测试,仿真结果与实际配载结果差异率不大,说明该研究方向对于集装箱配载方案优化是有效的.

Research on Container Ship Stowage Based on BP Neural Network

In view of the rapid and efficient demand of container ship’s stowage,according to the analysis of historical data by BP neural network,combined with the characteristics of container ship,the intelligent container stowage scheme is studied in depth.Based on the historical data of container ship stowage chart and the information of each unloading port,the BP neural network is used to train and optimize the historical data,mine the rules,so as to quickly generate the appropriate stowage position for all kinds of containers,optimize the loading operation,improve the stowage efficiency of container ships,and achieve the effect of cost saving.Finally,the real ship data are used to test,and the difference between the simulation results and the actual stowage results is small,which indicates that the research direction is effective for the optimization of container stowage scheme.