Online Variant of Parcel Allocation in Last-mile Delivery
This addresses the impracticality of offline methods for real-world last-mile delivery logistics, offering a solution for companies managing dynamic urban parcel delivery.
The paper tackles the problem of dynamically allocating parcels to crowd-workers in last-mile delivery by formalizing an online variant, where parcels are pre-placed and workers arrive dynamically, and proposes an algorithm with theoretical guarantees, showing effectiveness in experiments on real and synthetic datasets.
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.