Non-asymptotic near optimal algorithms for two sided matchings
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Abstract (arXiv)
A two-sided matching system is considered, where servers are assumed to arrive at a fixed rate, while the arrival rate of customers is modulated via a price-control mechanism. We analyse a loss model, wherein customers who are not served immediately upon arrival get blocked, as well as a queueing model, wherein customers wait in a queue until they receive service. The objective is to maximize the platform profit generated from matching servers and customers, subject to quality of service constraints, such as the expected wait time of servers in the loss system model, and the stability of the customer queue in the queuing model. For the loss system, subject to a certain relaxation, we show that the optimal policy has a bang-bang structure. We also derive approximation guarantees for simple pricing policies. For the queueing system, we propose a simple bi-modal matching strategy and show that it achieves near optimal profit.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 24 Jul 2022.
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- 2024Learning-Based Pricing and Matching for Two-Sided Queuescited by 1
- 2024Learning-Based Pricing and Matching for Two-Sided Queues17 Mar 2024