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- W3202674443 abstract "Matching demand (riders) to supply (drivers) efficiently is a fundamental problem for ride-hailing platforms who need to match the riders (almost) as soon as the request arrives with only partial knowledge about future ride requests. A myopic approach that computes an optimal matching for current requests ignoring future uncertainty can be highly sub-optimal. In this paper, we consider a two-stage robust optimization framework for this matching problem where future demand uncertainty is modeled using a set of demand scenarios (specified explicitly or implicitly). The goal is to match the current request to drivers (in the first stage) so that the cost of first stage matching and the worst-case cost over all scenarios for the second stage matching is minimized. We show that this two-stage robust matching is NP-hard under both explicit and implicit models of uncertainty. We present constant approximation algorithms for both models of uncertainty under different settings and show they improve significantly over standard greedy approaches." @default.
- W3202674443 created "2021-10-11" @default.
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- W3202674443 date "2021-01-01" @default.
- W3202674443 modified "2023-09-26" @default.
- W3202674443 title "Matching Drivers to Riders: A Two-Stage Robust Approach" @default.
- W3202674443 doi "https://doi.org/10.4230/lipics.approx/random.2021.12" @default.
- W3202674443 hasPublicationYear "2021" @default.
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