A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

Abstract

With advent of the postepidemic era, the development of digital logistics operations management is imminent. Among the various logistics delivery methods, same-city delivery is chosen by the vast majority of customers for its timeliness and safety. Online ordering and delivery methods for same-city delivery are also gaining increasing attention from enterprises which need to know the inventory balance of all same-city warehouses in time for early deployment and response. However, in practice, the inventory balance of each warehouse can be affected by other warehouses in the same city, and there is often a lack of data in the inventory management system due to equipment and other issues resulting in a poor response from the company to handle emergencies. To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. The L-curve criterion was used to select hyperparameter values, the spatiotemporal regularize was used to capture the time dependence of the time series, and the model performance was evaluated using root mean square error and mean absolute percentage error. Comparisons with classical interpolation techniques were made to validate the improved performance of the proposed method.