Abstract: Flexibility is a cornerstone of operations management, crucial to hedge stochasticity in product demands, service requirements, and resource allocation. In two-sided platforms, flexibility is also two-sided and can be viewed as the compatibility of agents on one side with agents on the other side. Platform actions often influence the flexibility on either the demand or the supply side. But how should flexibility be jointly allocated across different sides? Whereas the literature has traditionally focused on only one side at a time, our work initiates the study of two-sided flexibility in matching platforms. We propose a parsimonious matching model in random graphs and identify the flexibility allocation that optimizes the expected size of a maximum matching. Our findings reveal that flexibility allocation is a first-order issue: for a given flexibility budget, the resulting matching size can vary greatly depending on how the budget is allocated. Moreover, even in the simple and symmetric settings we study, the quest for the optimal allocation is complicated. In particular, easy and costly mistakes can be made if the flexibility decisions on the demand and supply side are optimized independently (e.g., by two different teams in the company), rather than jointly. To guide the search for optimal flexibility allocation, we uncover two effects -- flexibility cannibalization and flexibility asymmetry -- that govern when the optimal design places the flexibility budget only on one side or equally on both sides. In doing so we identify the study of two-sided flexibility as a significant aspect of platform efficiency.
Bio: Sébastien Martin is an assistant professor of operations at the Kellogg School of Management, Northwestern University. He received his M.Sc. in applied math from Ecole polytechnique (France) in 2015 and his Ph.D. in operations research from MIT in 2019. His research focuses on designing large-scale optimization algorithms for public sector operations and online platforms. His work has been recognized with two Franz Edelman Laureate Awards and the Dantzig Best Thesis Award. He designed Lyft's dispatch algorithm, optimized the school transportation systems of Boston and San Francisco, and created Kellogg's first AI teaching assistant.
