Abstract: 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 an online randomized policy that achieves a constant-factor guarantee close to the upper bound we establish for the performance of any online policy. Our policy as well as hardness results are parameterized by the minimum discrete hazard rate of the inter-activity time distribution. The design of our policy relies on sparsifying an ex-ante feasible solution by solving a sequence of dynamic programs. Further, in collaboration with Food Rescue U.S., a volunteer-based food recovery platform, we demonstrate the effectiveness of our policy by testing it on the platform’s data from various locations across the U.S.
Bio: Vahideh Manshadi is an Associate Professor of Operations at Yale School of Management. She is also affiliated with the Yale Institute for Network Science, the Department of Statistics and Data Science, and the Cowles Foundation for Research in Economics. Her current research focuses on the operation of online and matching platforms, especially those with profound societal impact, including volunteer crowdsourcing, organ allocation, and information (news) platforms. She has also worked on operational problems in e-commerce platforms such as online shopping and advertising. Her research draws on various computational disciplines, including machine learning, algorithm design, mechanism design, real-time optimization, and analysis of complex stochastic systems. Professor Manshadi serves on the editorial boards of Management Science, Operations Research, and Manufacturing & Service Operations Management. She received her Ph.D. in electrical engineering at Stanford University, where she also received MS degrees in statistics and electrical engineering. Before joining Yale, she was a postdoctoral scholar at the MIT Operations Research Center.