License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.13.2.1
URN: urn:nbn:de:0030-drops-191789
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19178/
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Megow, Nicole ; Moseley, Benjamin J. ; Shmoys, David ; Svensson, Ola ; Vassilvitskii, Sergei ; Schlöter, Jens
Weitere Beteiligte (Hrsg. etc.): Nicole Megow and Benjamin J. Moseley and David Shmoys and Ola Svensson and Sergei Vassilvitskii and Jens Schlöter

Scheduling (Dagstuhl Seminar 23061)

pdf-format:
dagrep_v013_i002_p001_23061.pdf (2 MB)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 23061 "Scheduling". The seminar focused on the emerging models for beyond-worst case algorithm design, in particular, recent approaches that incorporate learning. This includes models for the integration of learning into algorithm design that have been proposed recently and that have already demonstrated advances in the state-of-art for various scheduling applications: (i) scheduling with error-prone learned predictions, (ii) data-driven algorithm design, and (iii) stochastic and Bayesian learning in scheduling.

BibTeX - Entry

@Article{megow_et_al:DagRep.13.2.1,
  author =	{Megow, Nicole and Moseley, Benjamin J. and Shmoys, David and Svensson, Ola and Vassilvitskii, Sergei and Schl\"{o}ter, Jens},
  title =	{{Scheduling (Dagstuhl Seminar 23061)}},
  pages =	{1--19},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{13},
  number =	{2},
  editor =	{Megow, Nicole and Moseley, Benjamin J. and Shmoys, David and Svensson, Ola and Vassilvitskii, Sergei and Schl\"{o}ter, Jens},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19178},
  URN =		{urn:nbn:de:0030-drops-191789},
  doi =		{10.4230/DagRep.13.2.1},
  annote =	{Keywords: scheduling, mathematical optimization, approximation algorithms, learning methods, uncertainty}
}

Keywords: scheduling, mathematical optimization, approximation algorithms, learning methods, uncertainty
Collection: DagRep, Volume 13, Issue 2
Issue Date: 2023
Date of publication: 09.10.2023

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