Fair Task Allocation in Crowdsourced Delivery

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