Dynamic Matching in a Sharing Economy

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This research aims to study dynamic matching in a sharing economy to maximize the total social welfare, by (1) exploring structures of optimal policies and (2) designing efficiently computable heuristic policies.***To be specific, I propose to study the question of how to match supply with demand in a sharing economy under inter-temporal uncertainty on both sides. What is typically involved in a sharing economy is a two-sided market consisting of the supply and demand side and an intermediary market maker. There are different types of supply and demand. Matching different types of supply and demand incurs different costs. The intermediary organization/platform serves to coordinate supply and demand of different types. For example, United Network for Organ Sharing (UNOS) in the United States allocates donated organs to patients in need of transplantation. Given prices of supply are irrelevant (e.g., in organ transplantation) or exogenously given, matching at the operational level can provide an efficient way for the platform to allocate crowdsourced supply across different demand types inter-temporally. Practitioners of a sharing platform need quantitatively implementable tools to efficiently match supply and demand, taking into account that over time, there will be new arrivals of supply and demand to the market, and unmatched supply and demand may abandon the wait. ******Under a discrete-time dynamic programming formulation, I plan to (1) study the structures of the optimal matching policies. Then I plan to (2) derive heuristic policies, based on approximation methods, which the platform can implement to achieve near-optimal social welfare maximization. For certain heuristic policies, I plan to (3) derive their worst-case performance bounds. I suggest to extend the base formulation to 4 different directions, to account for (1) continuous-time arrivals of supply and demand, (2) endogenized pricing decisions, (3) competition between sharing platforms and (4) dynamic matching over a network. ******My proposed research is expected to make impacts. First, designing optimal inter-temporal matching policies would enable platforms to achieve higher efficiency and social welfare. I plan to seek possible test and implementation of the derived policies in currently operating sharing platforms. Second, the proposed general framework builds on inventory management (that focuses on the supply side) and revenue management (that focuses on the demand side), the two foundations of operations management/research. Given my publication record in the top operations journals, I am confident of having a couple of high-quality papers coming out of this research proposal (on dynamic matching of both the supply and demand side), which are expected to attract many follow-up works from the community of operations research and management science.***********