Dynamic Matching for On-Demand Service Platforms

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In the past few years, the boom of Uber-like two-sided on-demand service platforms has made a significant impact on people's everyday life. The matching mechanism that links supply and demand is a critical contributing component to the decision-making processes that occur within the platforms. For example, ride-sharing/-hailing services like Uber and Lyft match drivers with riders; crowdsourced delivery platforms such as Uber Eats and Amazon Flex match couriers with orders; freelancing platforms such as Upwork connect employers with freelancers for short-term employment. For most on-demand platforms, matching decisions must be made in real time, as both supply and demand are sensitive to delay. Moreover, there is a high degree of uncertainty associated with the arrival processes on both sides of the market. Due to those reasons, effective and efficient matching between supply and demand is both a difficult and essential task for the platforms. In this proposed research, I intend to study the following dynamic matching problems applicable to different platforms: ******(i). Centralized one-to-one matching. This is the problem faced by platforms such as Uber for their ride-hailing services (e.g., UberX and UberXL). The problem is difficult due to the heterogeneity in supply and demand characteristics (e.g., location, rating). I will develop a Markov decision process model to formulate the problem, and focus on algorithmic and computational studies for maximizing total expected matching rewards. In particular, I will develop approximate algorithms with performance guarantee and reinforcement learning methods to solve the problem efficiently.******(ii). Centralized many-to-one matching. Platforms such as ridesharing services (e.g., Uber Pool) and crowdsourced delivery services (e.g., Uber Eats) often assign multiple demand units to the same supplier. To formulate the problem, I propose a bi-level dynamic optimization framework. The outer-level solves the “matching” problem (i.e., the assignment of several demand units to a supplier), whereas the inner level solves the “routing” problem (e.g., finding a route to pickup and drop off riders by an Uber Pool driver). I aim to develop efficient approximate algorithms to compute the optimal matching and “routing” decisions.******(iii). Decentralized dynamic matching. In essence, platforms such as Upwork and Airbnb are marketplaces, where supply and demand match with each other in a decentralized way. In contrast with the economic matching theories, decentralized matching in on-demand platforms are more time-sensitive and associated with short-term rewards. I will formulate the problem as a sequential game and characterize its equilibrium. I will also investigate possible interventions by the platform to improve matching benefits.******Based on the above research projects, I will also study how improved matching efficiency impacts on society (e.g., how it affects traffic congestion, long-term job opportunities, etc.). **