Study of Impact of Moment Information in Demand Forecasting on Distributionally Robust Fulfillment Rate Improvement Algorithm

Front distribution centers are extensively employed in E-commerce distribution networks to shorten the delivery time, thereby stimulating customers’ purchase intentions and enhancing customer loyalty. When a customer places an order, the designated front distribution center quickly processes it to ensure prompt delivery. If the front distribution center is out of stock, the order will be fulfilled by its corresponding regional distribution center, which will result in a longer delivery time. Once the regional distribution center is also out of stock, a lost sale occurs. This paper improves a distributionally robust allocation model aimed at enhancing the fulfillment rates of front distribution centers while also preserving the overall fulfillment rate within the region. We reformulate this distributionally robust allocation model into an equivalent mixed-integer linear programming model and develop a corresponding approximation algorithm. Through numerical experiments, we comprehensively reveal the impact of moment information in demand forecasting on the distributionally robust fulfillment rate improvement algorithm by discovering how demand forecasting influences the allocation rule and how forecasted variance influences the fulfillment rates at fixed or changing inventory levels.