Personalized Recommendation of BOPS Self-Pickup Store Considering Customer Choice behavior
In the context of omnichannel retail,a method for recommending self-pickup locations for cus-tomers in the BOPS(Buy Online,Pick Up in Store)model has been proposed.This method constructs u-tility functions for customers under different delivery options,taking into account factors such as the shop-ping district,distance,and estimated delivery time.Customer choice behavior is characterized using an MNL(Multinomial Logit)selection model,and a mixed-integer programming model is formulated with the objective of maximizing retailer profit while considering customer choice behavior.Furthermore,the model is transformed into a mixed-integer second-order cone programming model that is amenable to solver-based solutions.Numerical experiments demonstrate that the recommended self-pickup location method proposed in this article can enhance the retailer's profit.Finally,the relationship between estimated delivery time,customer distance sensitivity,and revenue are explored.Numerical experimental results reveal a positive correlation between the retailer's total profit and estimated delivery time,as well as a negative correlation between the retailer's total profit and customer sensitivity to distance.
万方数据
维普
