https://www.dagstuhl.de/23061

05. – 10. Februar 2023, Dagstuhl-Seminar 23061

Scheduling

Organisatoren

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)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Christina Schwarz zu administrativen Fragen

Michael Gerke zu wissenschaftlichen Fragen

Dokumente

Programm des Dagstuhl-Seminars (Hochladen)

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Motivation

Scheduling is a major research field that is studied from a practical and theoretical perspective in computer science, mathematical optimization and operations research. Applications range from traditional production scheduling and project planning to the newly arising resource management tasks in the area of internet technology such as distributed cloud service networks and the allocation of virtual machines to physical servers. Algorithms for scheduling problems have been one of the richest areas of algorithmic research, spanning nearly 70 years of work. Throughout, research has been prompted by the fact that in most settings, these computational problems are quite challenging, and new approaches and frameworks are continually being added to help tackle a broadening portfolio of scheduling problems.

At this Dagstuhl Seminar, we focus on the emerging models for beyond-worst case algorithm design, in particular, recent approaches that incorporate learning. Several models for the integration of learning into algorithm design have been proposed and have already demonstrated advances in the state-of-art for various scheduling applications. This seminar will focus on three established themes:

  1. Scheduling with error-prone learned predictions
  2. Data-driven algorithm design
  3. Stochastic and bayesian learning in scheduling

The seminar aims to bring together researchers working on distinct areas to encourage cross-fertilization among different research directions. As the field of learning is very broad, we methodologically focus on the theoretical design of algorithms with provable performance guarantees.

Motivation text license
  Creative Commons BY 4.0
  Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, and Sergei Vassilvitskii

Dagstuhl-Seminar Series

Classification

  • Data Structures And Algorithms
  • Machine Learning

Keywords

  • Scheduling
  • Mathematical optimization
  • Approximation algorithms
  • Learning methods
  • Uncertainty

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.