Location Optimization Of Urban Distribution Centers In Cloud Inventory And O2O Mode

At present,the city's logistics and distribution industry has the characteristics of small single business volume and high distribution frequency.As the most important part of the logistics and transportation chain,the logistics distribution center has a direct impact on the level of logistics services and the company's profits and customers.However,there are still have some problems such as the incomplete delivery service system in distribution center.The logistics on“cloud warehousing” pays more attention to the characteristics of consumers compared to traditional logistics.At the same time,the O2 O model is an important business model for e-commerce,and development of O2 O is inseparable from the efficiency of logistics distribution.Therefore,this paper combines the “cloud warehousing” and O2 O models to optimize the location of existing physical stores and select the best physical store as a distribution center,so as to improve the efficiency of logistics distribution and reduce logistics costs.On the basis of summarizing related research results at home and abroad,this article systematically elaborates and analyzes the functions and classification of logistics distribution centers and the common factors and methods for site selection.Then,it thoroughly studied the characteristics of cloud storage,implementation routes,collaborative inventory mechanism,the definition of O2 O model,the difference between O2 O business model and traditional e-commerce,have the pros and cons analysis.Then,combined with logistics and distribution situation of S company,the preliminary plan of distribution center location model is given,and a city logistics distribution center location model is established.Assuming that the location of the regional distribution center is known,the location and quantity of the alternative store are known,and the demand,location,and quantity of the end demand point are known,the goal is to minimize the total logistics cost,and select from the alternative physical stores.The best candidate location,and determine the service coverage.This paper compares the commonly used algorithms theoretically,and finally adopts a genetic algorithm to solve the problem.According to the characteristics of the location model,a genetic algorithm with an embedded random algorithm is used,that is,the return of each end demand point is calculated in the genetic algorithm.Rate is generated using a random algorithm.Finally,it is programmed with MATLAB to test and analyze the data obtained by the genetic algorithm,and finally get the best distribution center location solution.