Allocation Problems in Ride-sharing Platforms

Bipartite-matching markets pair agents on one side of a market with agents, items, or contracts the opposing side. Prior work addresses online bipartite-matching markets, where arrive over time and are dynamically matched to known set disposable resources. In this article, we propose new model, Online Matching (offline) Reusable Resources under Known Adversarial Distributions ( OM-RR-KAD ) , in which resources offline reusable instead disposable; that is, once matched, become available again at some point future. We show our model is tractable by presenting an LP-based non-adaptive algorithm achieves competitive ratio ½-ϵ for any given constant ϵ > 0. also no adaptive can achieve ½ + o (1) based same benchmark LP. Through data-driven analysis massive openly dataset, robust enough capture application taxi dispatching services ride-sharing systems. present heuristics perform well practice.