Maximum Weight Online Matching with Deadlines
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Abstract (arXiv)
We study the problem of matching agents who arrive at a marketplace over time and leave after d time periods. Agents can only be matched while they are present in the marketplace. Each pair of agents can yield a different match value, and the planner's goal is to maximize the total value over a finite time horizon. First we study the case in which vertices arrive in an adversarial order. We provide a randomized 0.25-competitive algorithm building on a result by Feldman et al. (2009) and Lehman et al. (2006). We extend the model to the case in which departure times are drawn independently from a distribution with non-decreasing hazard rate, for which we establish a 1/8-competitive algorithm. When the arrival order is chosen uniformly at random, we show that a batching algorithm, which computes a maximum-weighted matching every (d+1) periods, is 0.279-competitive.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplacecited by 11
- 2023Fast and Efficient Matching Algorithm with Deadline Instancescited by 7
- 2026When to Commit and When to Defer: Maturing Markov Decision Processes under Refining Information and Expiring Opportunitiescited by 0
- 2026When to Commit and When to Defer: Maturing Markov Decision Processes under Refining Information and Expiring Opportunities17 Jun 2026
- 2023Fast and Efficient Matching Algorithm with Deadline Instances15 May 2023
- 2022Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace10 Feb 2022