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Title: Allocation Problems in Ride Sharing Platforms: Online Matching with Offline Reusable Resources
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 - epsilon for any given epsilon > 0. We also show that no non-adaptive algorithm can achieve a ratio of 1/2 + 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.  more » « less
Award ID(s):
1749864
NSF-PAR ID:
10065376
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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