Liner Operation Optimization Of The Yangtze River Based On Shipping Big Data

The Yangtze River shipping is a national strategic development.The shipping on the Yangtze River stem stream plays an important role in the transportation system of China,and it is a veritable golden waterway.With the completion of the Yangtze River Shipping Big Data Platform,the development of the Yangtze River Shipping Information Technology has entered the "big data" era.However,the charm of big data is not the scale of the data itself.How to make the Big Data resource as an important booster for the development of the Yangtze River shipping is the most concern for the Yangtze River shipping industry,after it had entered the era of big data.On the one hand,excavation of Yangtze River shipping big data is the key to realizing the value of data.Especially for the shipping companies of the Yangtze River,the data resources and services are always very scarce;on the other hand,traditional management methods must be updated to make more data resources can be used effectively,especially for shipping companies that are currently lagging behind in management.This article focuses on the optimization of shipping liner companies that operating in the Yangtze River in the era of big data and has carried out the following tasks.(1)A short-term freight traffic forecasting model considering time and space factors had been proposed.This model used the frequent pattern idea to mine the frequent ports of the airline network,and used the frequent ports to retain the main spatial information of the high-dimensional waterway network,and then used the neural network to fit the relationship of the space-time freight traffic between the frequent ports and the target port.The cargo volume forecasting accuracy of the model at various time granularity is higher than that only considering the time factor model,and can forecast short-term freight volume such as week and day.(2)An optimization model of the Yangtze River liner shipping line considering the fluctuate freight volume had been proposed.To solve the model,a two-stage solution algorithm combining genetic and improved particle swarm optimization was developed.This model aims at the optimal operational efficiency of routes,and can search for the most profitable route plans including the port of call,ship type,and space allocation under the condition of fluctuating weekly cargo traffic.(3)A model for predicting the residence time of a ship in a port had been proposed.Firstly,all data segments are collected to analyze the information required for the vessel’s residence time in the port.Then,based on the ship’s behavior characteristics,the data segments had integrated into the complete data required for the research,and then the shipping Characteristics The semi-parametric model had used to fit the ship’s dwell time in the port and carry out predictions.(4)Proposed an optimization model for the Yangtze Riverline liner considering the uncertain delay factors.This model aims at delays in the port delays and the detention of the Three Gorges ship lock,which are the two main Yangtze River liners.The class rate is a random chance constraint,and a stochastic chance constrained model had established for the Yangtze River liner ship schedule with compensation function.The stochastic simulation particle swarm algorithm had bedn used to search for the optimal solution of the model,and different classes,different delay penalties,and different operations had been used for instances.The shipbuilding optimization plan of the strategy was analyzed.The above study aims to optimize shipping line operations in the era of shipping big data,excavating the value of shipping big data,and applying it to the optimization of shipping lines and shipping schedules for liner operations,not only improving the service level of shipping big data,but also Yangtze River shipping companies provide scientific guidance for the operation and promote the healthy and rapid development of the Yangtze River shipping.