A Study on Predicting Key Times in the Takeout System’s Order Fulfillment Process - Dongyi Hu; Wei Deng; Zilong Jiang; Yong Shi (Systems)
With the rapid development of the Internet, businesses in the traditional catering industry are increasingly shifting toward the Online-to-Offline mode, as on-demand food delivery platforms continue to grow rapidly. Within these takeout systems, riders have a role throughout the order fulfillment process. Their behaviors involve multiple key time points, and accurately predicting these critical moments in advance is essential for enhancing both user retention and operational efficiency on such platforms. This paper first proposes a time chain simulation method, which simulates the order fulfillment in segments with an incremental process by combining dynamic and static information in the data. Subsequently, a GRU-Transformer architecture is presented, which is based on the Transformer incorporating the advantages of the Gated Recurrent Unit, thus working in concert with the time chain simulation and enabling efficient parallel prediction before order creation. Extensive experiments conducted on a real-world takeout food order dataset demonstrate that the Mean Squared Error of the prediction results of GRU-Transformer with time chain simulation is reduced by about 9.78% compared to the Transformer. Finally, according to the temporal inconsistency analysis, it can be seen that GRU-Transformer with time chain simulation still has a stable performance during peak periods, which is valuable for the intelligent takeout system.