Robust Route Planning under Uncertain Pickup Requests for Last-mile Delivery

Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.