Fair Task Allocation in Crowdsourced Delivery

TL;DR

This paper introduces F-Aware, a novel algorithm for crowdsourced delivery that balances fairness to workers with maximizing task allocation, significantly improving efficiency and fairness over existing methods.

Contribution

The paper presents a new 2-phase allocation model and the F-Aware algorithm, which incorporate fairness into task assignment for crowdsourced delivery, addressing limitations of previous solutions.

Findings

F-Aware runs around 10^7 times faster than TAR-optimal solutions.

F-Aware allocates 96.9% of possible tasks, demonstrating high efficiency.

F-Aware provides a more fair distribution of tasks compared to competitors.

Abstract

Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries, including online shopping, on-demand local delivery, and on-demand transportation. The power of crowdsourced delivery stems from the large number of workers potentially available to provide services and reduce costs. It has been shown in social psychology literature that fairness is key to ensuring high worker participation. However, existing assignment solutions fall short on modeling the dynamic fairness metric. In this work, we introduce a new assignment strategy for crowdsourced delivery tasks. This strategy takes fairness towards workers into consideration, while maximizing the task allocation ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase allocation model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that minimizes unfairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task allocation ratio (TAR), as well as unfairness and assignment ratio. Experiments show that F-Aware runs around 10^7 x faster than the TAR-optimal solution and allocates 96.9% of the tasks that can be allocated by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm.