Service Time Prediction for Delivery Tasks via Spatial Meta-Learning
Sijie Ruan, Cheng Long, Zhipeng Ma, Jie Bao, Tianfu He, Ruiyuan Li, Yiheng Chen, Shengnan Wu, Yu Zheng
Abstract
Service time is a part of time cost in the last-mile delivery, which is the time spent on delivering parcels at a certain location. Predicting the service time is fundamental for many downstream logistics applications, e.g., route planning with time windows, courier workload balancing and delivery time prediction. Nevertheless, it is non-trivial given the complex delivery circumstances, location heterogeneity, and skewed observations in space. The existing solution trains a supervised model based on aggregated features extracted from parcels to deliver, which cannot handle above challenges well. In this paper, we propose MetaSTP, a meta-learning based neural network model to predict the service time. MetaSTP treats the service time prediction at each location as a learning task, leverages a Transformer-based representation layer to encode the complex delivery circumstances, and devises a model-based meta-learning method enhanced by location prior knowledge to reserve the uniqueness of each location and handle the imbalanced distribution issue. Experiments show MetaSTP outperforms baselines by at least 9.5% and 7.6% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP is deployed and used internally in JD Logistics.