Online Variant of Parcel Allocation in Last-mile Delivery

TL;DR

This paper addresses the online parcel allocation problem in last-mile delivery, proposing a new algorithm with theoretical guarantees and validating its effectiveness through experiments on real and synthetic data.

Contribution

It formalizes the online parcel allocation problem in last-mile delivery and introduces a novel algorithm with proven theoretical guarantees.

Findings

The proposed algorithm performs effectively on real datasets.

The method provides theoretical guarantees for online parcel allocation.

Experimental results show efficiency and robustness of the approach.

Abstract

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.