Study On Location-capacity Decision Of Fresh Pre-position Warehouse Considering Consumer's Perception Of Freshness

The development of fresh e-commerce is being gradually mature,and the frequency of consumers consuming fresh products online is increasing.However,with the improvement of living standard,the attention is catched by the quality of fresh products and the timeliness of distribution.Therefore,for making the personalized needs of consumers satisfied,fresh products are gradually transferred from the traditional single warehouse throughout the country or multiple distribution centers to the mode of community distribution.However,the location and capacity of the pre-position warehouse mostly depend on the experience decision,which is difficult to manage,not matching supply and demand,and high distribution cost,which is not conducive to the development of fresh e-commerce.Therefore,it is particularly important to scientifically and rationally arrange the pre-position warehouse and optimize the inventory capacity.In this paper,a two-stage decision making method combining clustering algorithm and fuzzy programming model is proposed,which tries to solve the problem of location-capacity decision optimization from the perspectives of fresh product freshness and consumer perceived value.In the first stage,a preliminary decision method of the pre-position warehouse location selection and demand point allocation based on AP(Affinity Propagation)clustering algorithm was proposed.In the second stage,a fine decision model of location-capacity joint optimization is proposed,and an immune optimization algorithm is designed to solve the problem.The main research work are as follows:(1)This paper studies the preliminary decision of the pre-position warehouse location-demand point allocation.The AP clustering algorithm is used to cluster a large number of demand points according to their location characteristics and the relationship between the points.It is not essential for AP algorithm to set the number of clustering centers to obtain the layout of clustering centers.Demand point group can be obtained by AP primary clustering,and the number and location of the candidate pre-position warehouse can be obtained by AP secondary clustering,which constitutes the distribution network structure relationship of “the pre-position and demand point”,and lays a foundation for the detailed decision making work in the second stage.(2)The concept of fresh storage rate is put forward,and the pre-position capacity and operation model are analyzed,aiming to build a fine decision model of joint optimization of the pre-position warehouse location and capacity.(3)This paper studies the detailed decision of the pre-position warehouse location location and capacity for consumer demand.Combined with the location and capacity of the pre-position warehouse,the perception of consumer freshness and the storage freshness rate are considered to conduct a model of pre-position warehouse location-capacity and demand point distribution is constructed with the lowest total cost as the objective function.In combination with the above research work,the example is analyzed.An example is given to verify the decision making method.Immune optimization algorithm is designed.Crossover and mutation operations are improved to perform sensitivity analysis on the fresh storage rate.The paper analyzes the change trend of replenishment cost,operation cost,construction cost and replenishment cycle with the fresh storage rate of the pre-position warehouse to obtain reasonable site selection scheme,capacity and operation scheme to provide a reasonable basis for fresh e-commerce enterprises to conduct the pre-position warehouse selection work.