Online Policies for Efficient Volunteer Crowdsourcing

Nonprofit crowdsourcing platforms such as food recovery organizations rely on volunteers to perform time-sensitive tasks. Thus, their success crucially depends on efficient volunteer utilization and engagement. To encourage volunteers to complete a task, platforms use nudging mechanisms to notify a subset of volunteers with the hope that at least one of them responds positively. However, since excessive notifications may reduce volunteer engagement, the platform faces a trade-off between notifying more volunteers for the current task and saving them for future ones. Motivated by these applications, we introduce the online volunteer notification problem, a generalization of online stochastic bipartite matching where tasks arrive following a known time-varying distribution over task types. Upon arrival of a task, the platform notifies a subset of volunteers with the objective of minimizing the number of missed tasks. To capture each volunteer's adverse reaction to excessive notifications, we assume that a notification triggers a random period of inactivity, during which she will ignore all notifications. However, if a volunteer is active and notified, she will perform the task with a given pair-specific match probability that captures her preference for the task. We develop two online randomized policies that achieve constant-factor guarantees which are close to the upper-bounds we establish for the performance of any online policy. Our policies as well as hardness results are parameterized by the minimum discrete hazard rate of the inter-activity time distribution. The design of our policies relies on two modifications of an ex-ante feasible solution: (1) properly scaling down the notification probability prescribed by the ex-ante solution, and (2) sparsifying that solution. Further, in collaboration with Food Rescue U.S., a volunteer-based food recovery platform, we demonstrate the effectiveness of our policies by testing them on the platform's data from various locations across the U.S.