Study On Location Of E-commercial Fresh Community Distribution Station Based On Customers' Utility

With the rapid development of E-commerce,the shopping method of consumers has changed.More and more consumers tend to choose online shopping which is convenient and quick.Fresh food,as a necessary ingredient of national life,has become a key competition market of E-commerce enterprises.The transportation of fresh food is strict due to the characteristics of perishability and short shelf life.Therefore,the new pattern of community distribution station has been put forward by some fresh e-commerce enterprises and cold chain logistics companies,that is,to establish community distribution stations at the end of the city in order to respond to the needs of consumers quickly.So,how to choose the location of fresh community distribution stations has become the first problem faced by fresh e-commerce enterprises.Scientific and reasonable site selection should be considered not only the cost of investment,but also the satisfaction of fresh online shopping users so as to maximize the utility of fresh online shopping.This paper introduces the relevant literature of cold chain logistics and fresh distribution station location both domestic and foreign,and summarizes the shortcomings of existing research,then builds the location model of e-commercial fresh community distribution station.Firstly,according to the characteristics of cold chain logistics,the cost composition of distribution network is analyzed,and the location cost model including fixed cost,operation cost and distribution cost is structured.Secondly,by introducing the random utility theory and analyzing the fresh online shopping behavior of users,the delivery time,food quality and shopping convenience are selected as the characteristic variables of the selection scheme,and the binary discrete selection model for fresh online shopping and fresh market shopping is established.The overall satisfaction of customers is expressed by the probability of customers choosing fresh online shopping.Finally,a multi-objective location model with the objective of minimizing the total cost of location and maximizing the probability of customers choosing fresh online shopping is structured.The multi-objective model is processed by linear weighting method,and the genetic algorithm is designed to solve the model.Combined with the case study of Dalian,the relevant data is collected,and the parameters of utility function in discrete choice models is fitted by SP survey.Then,the location schemes of the multi-objective model and the single-objective location model are obtained by genetic algorithm.The result shows that with the increase of the probability of customers choosing fresh online shopping,the cost of input increases at the same time.In contrast,the multi-objective location model considering customer utility has a higher return on investment by reducing cost input and improving customer's online shopping utility,which further verifies the feasibility and practical value of this study.