Online Variant of Parcel Allocation in Last-Mile Delivery

We investigate the problem of last-mile delivery, where a large amount of crowd-workers have performed a great quantity of location-specific urban logistics parcels. Current existing approaches mainly focus on offline scenarios, where all the spatial-temporal information of parcels and workers are given. However, the offline scenarios can be impractical since parcels and workers appear dynamically in reality, and the information of workers is unknown in advance. In this paper, we study the problem of last-mile delivery on online scenarios to resolve the shortcomings of the offline setting. We first formalize the online parcel allocation in last-mile delivery problem, where all parcels were put in pop-stations in advance, and workers arrive dynamically. Then we propose a baseline algorithm with no competitive ratio, and an algorithm providing theoretical guarantee for the parcel allocation in last-mile delivery. Finally, we verify the effectiveness and efficiency of proposed algorithms through extensive experiments on real and synthetic datasets.