OPTI: Order Preparation Time Inference for On-demand Delivery

Abstract

On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens’ daily lives significantly. In the fulfillment cycle, the order preparation time estimation is extremely important and can be used for many applications, such as improving order dispatching and fulfillment time estimation. Existing work is generally based on high-cost physical devices or large-scale labeled training data, which are not feasible in on-demand delivery services. We solve this problem based on already collected different kinds of data from the on-demand delivery platform, e.g., the courier’s reported arrival time to the merchant. Our intuition is that the couriers’ reported time implicitly reflects the order preparation time, which leads to a challenge: complicated correlations between the couriers’ reported arrival time and the order preparation time. To solve this challenge, we design an order preparation time inference framework OPTI, which first constructs a self-supervised classification task based on the couriers’ reported arrival time to infer the coarse-grained order preparation time and then exploits semi-supervised learning to transfer the coarse-grained time to fine-grained time inference. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.