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Research On Demand Forecast And Functional Areas Layout Planning Of Dingzhou Railway Logisitics Park

Posted on:2022-04-26Degree:MasterType:Thesis
Country:ChinaCandidate:C C GuoFull Text:PDF
GTID:2492306563965959Subject:Logistics Engineering
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With the continuous deepening of the transformation of Chinese social and economic and the much more intense competition in the cargo transportation market,railway freight transportation imminently wants to accelerate the transformation of modern logistics,consummate logistics service functions and promote service levels.In recent years,in order to achieve an all-round quality promotion of its own logistics services,the Railway Corporation comprehensively deploys logistics hub network construction,and promote the formation of a high-quality railway logistics infrastructure network system.As an important hub of the railway freight system,the railway logistics park is a key link for railway freight to broaden its service scope and increase its transport market share.Its internal layout will directly affect the operational efficiency of the logistics system.Based on a large number of research literatures on demand forecasting and layout planning in the domestic and overseas,putting theory into practice,this paper starts from the Dingzhou Railway Logistics Park example,and systematically studies the functional areas layout planning of the railway logistics park:(1)Briefly describe the relevant theories of railway logistics parks.By analyzing the intension,features and current situation of development of the railway logistics park,the objectives,principles and basic ideas of the railway logistics park layout planning are expounded.(2)Analyze the basic conditions and functions of the Dingzhou Railway Logistics Park.Summarize the basic conditions of the Dingzhou Railway Logistics Park’s construction background,advantageous conditions,and expected cargo categories,and analyze its functional positioning from three levels of macro social functions,basic business functions,and value-added service functions.(3)Design the basic ideas of the railway logistics park demand forecast,and complete the demand forecast of the Dingzhou Railway Logistics Park.Firstly,a city’s comprehensive strength assessment indicator system is built,and the Entropy Weight Method and Gravity Model are used to confirm the attractive region and attraction scale of freight volume.Secondly,the Pearson Correlation Analysis Method is used to remove the contributing factors that are weakly related to freight volume.And then,use the GM(1,1)Model to predict the contributing factors of freight volume of attractive cities,and use the GA-BP Model to predict freight volume of cities in the park’s attractive region,and use the GM(1,1)-Markov Model to predict rail freight volume of the city where the park is located.The method of estimating the volume of goods assumed by the railway logistics park is described.Finally,the above methods are used to obtain the amount of goods that the Dingzhou Railway Logistics Park will undertake in 2025.(4)Research on the layout planning of the functional areas of the railway logistics park,and obtain the optimal layout plan of the Dingzhou Railway Logistics Park.The correlation analysis of the functional areas of railway logistics parks is studied by the improved SLP method.An optimization model of the functional areas of railway logistics parks considering existing railway lines is constructed,and a corresponding Multiple Population Genetic Algorithm is a solution to this model.Because of the algorithm’s characteristics of co-evolution of multiple populations,comparing with standard genetic algorithm,it is determined that the solving efficiency of Multiple Population Genetic Algorithm is higher.Finally,the above methods are used to calculate the optimal layout plan of Dingzhou Railway Logistics Park,and the plan is appropriately adjusted and evaluated based on the actual situation.This article contains 30 figures,62 tables,and 76 references.
Keywords/Search Tags:Railway Logistics Park, Demand Forecast, Layout Planning, GA-BP Model, Improved SLP, Multiple Population Genetic Algorithm
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