Dynamic Matching in a Sharing Economy

本研究旨在通过(1)探索最优政策的结构，(2)设计可高效计算的启发式政策，研究共享经济中的动态匹配，以最大化社会总福利。具体而言，我建议研究在双方都存在跨期不确定性的情况下，共享经济中如何匹配供给与需求的问题。共享经济通常涉及的是一个由供需双方和中间做市商组成的双边市场。有不同类型的供给和需求。匹配不同类型的供给和需求会产生不同的成本。中介组织/平台的作用是协调不同类型的供求关系。例如，美国的器官共享联合网络（UNOS）将捐赠的器官分配给需要移植的患者。在供应价格不相关（如器官移植）或外源性给定的情况下，业务层面的匹配可以为平台在不同需求类型间分配众包供应提供一种有效的方式。共享平台的从业者需要量化可执行的工具来有效匹配供需，同时考虑到随着时间的推移，市场上还会出现新的供需，不匹配的供需可能会放弃等待。******在离散时间动态规划公式下，我计划(1)研究最优匹配策略的结构。然后，我计划(2)基于近似方法推导启发式策略，平台可以实施这些策略以实现接近最优的社会福利最大化。对于某些启发式策略，我计划(3)推导它们的最坏情况性能界限。我建议将基本公式扩展到4个不同的方向，以考虑(1)供应和需求的持续到达，(2)内生定价决策，(3)共享平台之间的竞争以及(4)网络上的动态匹配。******我提出的研究预计会产生影响。首先，设计最优的跨期匹配政策将使平台实现更高的效率和社会福利。我计划在目前运营的共享平台中寻求可能的测试和实施衍生的政策。其次，拟议的总体框架建立在库存管理（侧重于供应方）和收入管理（侧重于需求方）的基础上，这是运营管理/研究的两个基础。鉴于我在顶级运筹学期刊的发表记录，我有信心从这个研究计划中得出几篇高质量的论文（关于供需侧的动态匹配），有望吸引运筹学和管理科学领域的许多后续作品。***********

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.***********