The Research On Optimization Of Underground Logistics Network Based On Steiner Minimum Tree

With the exploitation of urban ground space becoming saturated,and the appearance of traffic congestion,population density,environmental degradation and many other urban diseases,the function of Chinese cities will gradually shift to the underground in the future to release the ground space and form a huge underground three-dimensional urban spatial structure.Among them,underground storage,underground logistics and distribution will be transferred to the underground.These logistics nodes and routes form an intricate underground logistics system.Therefore,this paper focuses on the network optimization problem of underground logistics,aims to minimize the network path,and construct the optimal network topology of underground logistics,which is of great significance to alleviate the traffic pressure on the ground,improve logistics efficiency and reduce logistics costs.However,the network optimization of underground logistics is easily affected by factors such as construction and operation costs,path length and underground conditions.There are problems such as location selection,path planning and model design of logistics nodes,and these problems are related to each other and work together on underground logistics networks.Therefore,this paper considers the whole and improves from the following two aspects: The first,the disjunction between facility location and vehicle routing problem in underground logistics network optimization;the second,the disjunction between model algorithm optimization and actual situation.In order to solve the above problems,according to the characteristics of underground space,this paper proposes to adopt Steiner Minimum Tree to build the underground logistics network optimization model.In the algorithm,Plant Growth Simulation Algorithm has the characteristics of high accuracy,good stability,fast running speed and easy programming.Therefore,this paper chooses Plant Growth Simulation Algorithm to solve and verify.For improving accuracy and reducing the complexity of problem calculation,on the basis of the original Plant Growth Simulation Algorithm,this paper puts forward to use Delaunay triangulation method and minimum spanning tree method to determine the topological structure.Afterwards,a series of data from STEINLIB and OR-Library are used to test the improved Plant Growth Simulation Algorithm.The results are compared with those of genetic algorithm and hybrid intelligent algorithm using Steiner's ratio as a measure standard,and the optimization results have been greatly improved,which proves the effectiveness and accuracy of the improved Plant Growth Simulation Algorithm.This paper applies the research results to Beijing and Xiongan New Area: The first,select several key logistics nodes of Beijing to construct the logistics dedicated underpass,and compared to spanning minimum tree,it improves greatly,which can save a lot of transportation costs;the second,simulate the topological structure of the 50 points and place it into the Xiongan New Area to construct the underground network system.Finally,through simulation experiment,it is proved that the proposed algorithm can reduce logistics costs,alleviate urban traffic congestion,and reduce pollution,etc.,which provides reference for the optimization of underground logistics network in the mega-central cities represented by Beijing and the new planned structure's cities represented by Xiongan New Area,and provides an example for future urban underground planning.