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Scheduling (Dagstuhl Seminar 23061)

Authors Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, Sergei Vassilvitskii, Jens Schlöter and all authors of the abstracts in this report

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DagRep.13.2.1.pdf
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  • 19 pages

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorial optimization
  • Theory of computation → Approximation algorithms analysis
Keywords
  • scheduling
  • mathematical optimization
  • approximation algorithms
  • learning methods
  • uncertainty

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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.

Cite As Get BibTex

Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, Sergei Vassilvitskii, and Jens Schlöter. Scheduling (Dagstuhl Seminar 23061). In Dagstuhl Reports, Volume 13, Issue 2, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/DagRep.13.2.1

Author Details

Nicole Megow
  • Universität Bremen, DE
Benjamin J. Moseley
  • Carnegie Mellon University - Pittsburgh, US
David Shmoys
  • Cornell University - Ithaca, US
Ola Svensson
  • EPFL - Lausanne, CH
Sergei Vassilvitskii
  • Google - New York, US
Jens Schlöter
  • Universität Bremen, DE
and all authors of the abstracts in this report
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