OPTI: Order Preparation Time Inference for On-demand Delivery

On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens' daily lives significantly. Different from traditional logistics services, e.g., FedEx and UPS, on-demand delivery is expected to be completed within a relatively short time, e.g., 30 minutes to 1 hour. In the fulfillment cycle, the order preparation time estimation is extremely important, which 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 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. We implement and evaluate OPTI in the [anonymous] platform, which is one of the largest on-demand delivery platforms in the world. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.