License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2021.23
URN: urn:nbn:de:0030-drops-147163
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14716/
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Brubach, Brian ; Grammel, Nathaniel ; Harris, David G. ; Srinivasan, Aravind ; Tsepenekas, Leonidas ; Vullikanti, Anil

Approximating Two-Stage Stochastic Supplier Problems

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LIPIcs-APPROX23.pdf (0.8 MB)

Abstract

The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint. We also explore a number of variants where additional constraints are imposed on the first-stage decisions, specifically matroid and multi-knapsack constraints.
Our eventual goal is to provide results for supplier problems in the most general distributional setting, where there is only black-box access to the underlying distribution. To that end, we follow a two-step approach. First, we develop algorithms for a restricted version of each problem, in which all possible scenarios are explicitly provided; second, we employ a novel scenario-discarding variant of the standard Sample Average Approximation (SAA) method, in which we crucially exploit properties of the restricted-case algorithms. We finally note that the scenario-discarding modification to the SAA method is necessary in order to optimize over the radius.

BibTeX - Entry

@InProceedings{brubach_et_al:LIPIcs.APPROX/RANDOM.2021.23,
  author =	{Brubach, Brian and Grammel, Nathaniel and Harris, David G. and Srinivasan, Aravind and Tsepenekas, Leonidas and Vullikanti, Anil},
  title =	{{Approximating Two-Stage Stochastic Supplier Problems}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)},
  pages =	{23:1--23:22},
  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/opus/volltexte/2021/14716},
  URN =		{urn:nbn:de:0030-drops-147163},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2021.23},
  annote =	{Keywords: Approximation Algorithms, Stochastic Optimization, Two-Stage Recourse Model, Clustering Problems, Knapsack Supplier}
}

Keywords: Approximation Algorithms, Stochastic Optimization, Two-Stage Recourse Model, Clustering Problems, Knapsack Supplier
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)
Issue Date: 2021
Date of publication: 15.09.2021

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