  We investigate the problem of last-mile delivery, where a large pool of
citizen crowd-workers are hired to perform a variety of location-specific urban
logistics parcel delivering tasks. Current approaches focus on offline
scenarios, where all the spatio temporal information of parcels and workers are
given. However, the offline scenarios can be impractical since parcels and
workers appear dynamically in real applications, and their information is
unknown in advance. In this paper, in order to solve 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, while
workers arrive dynamically. Then we propose an algorithm which provides
theoretical guarantee for the parcel allocation in last-mile delivery. Finally,
we verify the effectiveness and efficiency of the proposed method through
extensive experiments on real and synthetic datasets.
