Research On Store Location Of Fresh E-Commerce Enterprises Under New Retail

Fresh e-commerce,known as the "last blue ocean of e-commerce",is becoming more and more mature in China.The new online and offline retail model will become the main development direction in the future.Fresh products are a necessity in the daily life of Chinese residents.The market scale is huge.If fresh e-commerce enterprises want to occupy a larger market share and enhance customer stickiness,they should pay attention to customers’ shopping experience and constantly meet customers’ shopping needs.The offline stores of new retail Fresh e-commerce enterprises with the function of distribution center are an important hub connecting enterprises and customers.Their location should not only meet the actual cost and economic benefits of enterprises,but also meet the requirements of consumers for commodity quality and distribution timeliness.The reasonable location of offline stores plays an extremely important role in establishing an efficient and fast new retail Fresh logistics network and realizing economies of scale.In this context,this paper first systematically combs the location of offline stores of new retail Fresh e-commerce,and makes an in-depth study on the relevant factors affecting its location.Based on the analysis and summary of the existing location theory,a bilevel programming model for the location of offline stores of new retail Fresh e-commerce is constructed: the goal of the upper planning is to minimize the total cost of the enterprise,and take the time penalty cost and operation cost into account,so as to determine the optimal location;The goal of lower level planning is to maximize customer utility and determine the distribution between demand points and stores under the condition of considering service payment.Secondly,the hybrid genetic algorithm combining adaptive genetic algorithm and branch and bound algorithm is innovatively used to solve the bilevel programming model.The effectiveness of the model and algorithm is verified by a case.The results show that compared with the traditional genetic algorithm,the new hybrid genetic algorithm has better convergence and effectiveness,less iterations,and can obtain a more accurate global optimal solution.Finally,taking MISS-FRESH(part of Shanghai)as an example,the location of its offline stores is studied by using bilevel programming model and hybrid genetic algorithm.The results of the model show that the location optimization of stores according to the location method in this paper can not only save the cost of enterprises,but also improve the utility of customer experience.The overall service level has also improved.