Fair task allocation in crowdsourced delivery

Abstract

Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries. In this work, we introduce a new crowdsourced delivery platform that takes fairness towards workers into consideration, while maximizing the task completion ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase assignment 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 maximizes fairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task completion ratio, as well as fairness and assignment ratio. Experiments show that F-Aware runs around 107× faster than the TAR-optimal solution and assigns 96.9% of the tasks that can be assigned 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. IEEE