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APPROX
Matching Drivers to Riders: A Two-Stage Robust Approach

Authors: Omar El Housni, Vineet Goyal, Oussama Hanguir, and Clifford Stein

Published in: LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)


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.

Cite as

Omar El Housni, Vineet Goyal, Oussama Hanguir, and Clifford Stein. Matching Drivers to Riders: A Two-Stage Robust Approach. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 12:1-12:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


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@InProceedings{housni_et_al:LIPIcs.APPROX/RANDOM.2021.12,
  author =	{Housni, Omar El and Goyal, Vineet and Hanguir, Oussama and Stein, Clifford},
  title =	{{Matching Drivers to Riders: A Two-Stage Robust Approach}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)},
  pages =	{12:1--12:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-207-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{207},
  editor =	{Wootters, Mary and Sanit\`{a}, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.12},
  URN =		{urn:nbn:de:0030-drops-147054},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2021.12},
  annote =	{Keywords: matching, robust optimization, approximation algorithms}
}
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