Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based adaptive algorithm that achieves an online competitive ratio of 1 2 − for any given > 0. We also show that no non-adaptive algorithm can achieve a ratio of + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture the application of taxi dispatching services and ridesharing systems. We also present heuristics that perform well in practice.
Earlier, Dickerson et al. REF considered another simulation and LP based approach for a different setting of online matching with reusable resources and also showed a 1/2-approximation.
Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources
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Computer Science