Research&Realization On Logistics Management Information System Based On Clustering

Over the past few years, the fast development of logistics enterprises has drawn great attention from every profession and trade, and the combination of logistics and information and network technology speeds up the modernization of logistics. The most crucial link of logistics is logistics distribution, which influences the quality of logistics directly. Therefore, highly efficient, reasonable and scientific distribution is the foundation of premium logistics.The main content of this thesis is the design and realization of a logistics management information system, providing convenient, real-time and secure services, and presenting logistics distribution flows to the customers transparently. The difficulties in distribution lie in the determination of distribution routes, which can be reduced to VRP (Vehicle Routing Problems), that was raised and focused by scholars in1950s and lots of methods have been applied to. One of the effective solutions is to divide VRP into independent TSP (Traveling Saleman Problems) and then solve them separately, which is a typical two-phase algorithm. The principle of division is making sure that there is no intersection between sub-problems and the data of sub-problem should be more concentrated inside sub-problem and more different between sub-problems:Clustering Analysis is just right for the problems.In the first stage, the division of VRP will be realized by IDBSCAN, which is extended from DBSCAN. The input of IDBSCAN algorithm is geographic data. For better simulation, the distance between two points will be measured by practical driving distance, and all data will be weighed by factor analysis to reflect the difference between regions. DBSCAN is sensitive to neighborhood parameters Eps and is hard to discover large density fluctuation cluster. In order to overcome the two shortcomings, IDBSCAN determines density interval of data by analyzing the neighborhood distribution of data; in the process of clustering, neighborhood value of data will be mapped on density interval to determine whether it is the core, and then clustering it in the method of DBSCAN algorithm. The outputs of IDBSCAN are independent clusters.In the second stage, all TSP in clusters will be solved by Ant Colony Optimization algorithm and the solution set is the ultimate distribution route.In conclusion, the functions of logistics management information system and the application of two-phase algorithm in this system will be described.