2 Search Results for "Goyal, Vineet"


Document
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)


Copy BibTex To Clipboard

@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}
}
Document
Stochastic and Robust Scheduling in the Cloud

Authors: Lin Chen, Nicole Megow, Roman Rischke, and Leen Stougie

Published in: LIPIcs, Volume 40, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)


Abstract
Users of cloud computing services are offered rapid access to computing resources via the Internet. Cloud providers use different pricing options such as (i) time slot reservation in advance at a fixed price and (ii) on-demand service at a (hourly) pay-as-used basis. Choosing the best combination of pricing options is a challenging task for users, in particular, when the instantiation of computing jobs underlies uncertainty. We propose a natural model for two-stage scheduling under uncertainty that captures such resource provisioning and scheduling problem in the cloud. Reserving a time unit for processing jobs incurs some cost, which depends on when the reservation is made: a priori decisions, based only on distributional information, are much cheaper than on-demand decisions when the actual scenario is known. We consider both stochastic and robust versions of scheduling unrelated machines with objectives of minimizing the sum of weighted completion times and the makespan. Our main contribution is an (8+eps)-approximation algorithm for the min-sum objective for the stochastic polynomial-scenario model. The same technique gives a (7.11+eps)-approximation for minimizing the makespan. The key ingredient is an LP-based separation of jobs and time slots to be considered in either the first or the second stage only, and then approximately solving the separated problems. At the expense of another epsilon our results hold for any arbitrary scenario distribution given by means of a black-box. Our techniques also yield approximation algorithms for robust two-stage scheduling.

Cite as

Lin Chen, Nicole Megow, Roman Rischke, and Leen Stougie. Stochastic and Robust Scheduling in the Cloud. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 40, pp. 175-186, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


Copy BibTex To Clipboard

@InProceedings{chen_et_al:LIPIcs.APPROX-RANDOM.2015.175,
  author =	{Chen, Lin and Megow, Nicole and Rischke, Roman and Stougie, Leen},
  title =	{{Stochastic and Robust Scheduling in the Cloud}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)},
  pages =	{175--186},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-89-7},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{40},
  editor =	{Garg, Naveen and Jansen, Klaus and Rao, Anup and Rolim, Jos\'{e} D. P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.175},
  URN =		{urn:nbn:de:0030-drops-53028},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2015.175},
  annote =	{Keywords: Approximation Algorithms, Robust Optimization, Stochastic Optimization, Unrelated Machine Scheduling, Cloud Computing}
}
  • Refine by Author
  • 1 Chen, Lin
  • 1 Goyal, Vineet
  • 1 Hanguir, Oussama
  • 1 Housni, Omar El
  • 1 Megow, Nicole
  • Show More...

  • Refine by Classification
  • 1 Theory of computation → Approximation algorithms analysis

  • Refine by Keyword
  • 1 Approximation Algorithms
  • 1 Cloud Computing
  • 1 Robust Optimization
  • 1 Stochastic Optimization
  • 1 Unrelated Machine Scheduling
  • Show More...

  • Refine by Type
  • 2 document

  • Refine by Publication Year
  • 1 2015
  • 1 2021

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail