Dynamic Type Matching
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Abstract (arXiv)
We consider an intermediary's problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. More specifically, there are two disjoint sets of demand and supply types, and a reward associated with each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform's problem is to decide on the optimal matching policy to maximize the total discounted rewards minus costs, given that unmatched demand and supply will incur waiting or holding costs, and will be carried over to the next period (with abandonment). For this dynamic matching problem, we provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. As a result of the priority property, the optimal matching policy boils down to a match-down-to threshold structure when considering a specific pair of demand and supply types in the priority hierarchy.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
8 papers.
- 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
- 2022Learn to Match with No Regret: Reinforcement Learning in Markov Matching Marketscited by 16
- 2023A Better Match for Drivers and Riders: Reinforcement Learning at Lyftcited by 9
- 2024An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailingcited by 7
- 2021Subgroup Fairness in Two-Sided Marketscited by 3
- 2021Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Surveycited by 3
- 2024Learning-Based Pricing and Matching for Two-Sided Queuescited by 1
- 2026Dynamic resource matching in manufacturing using deep reinforcement learningcited by 0
- 2026Dynamic resource matching in manufacturing using deep reinforcement learning28 Mar 2026
- 2024An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing20 Aug 2024
- 2024Learning-Based Pricing and Matching for Two-Sided Queues17 Mar 2024
- 2023A Better Match for Drivers and Riders: Reinforcement Learning at Lyft20 Oct 2023
- 2022Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets7 Mar 2022
- 2021Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey10 Aug 2021
- 2021Subgroup Fairness in Two-Sided Markets4 Jun 2021
- 2021Reinforcement Learning for Ridesharing: An Extended Survey3 May 2021