https://www.dagstuhl.de/23061

February 5 – 10 , 2023, Dagstuhl Seminar 23061

Scheduling

Organizers

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)

For support, please contact

Christina Schwarz for administrative matters

Michael Gerke for scientific matters

Documents

Dagstuhl Seminar Schedule (Upload here)

(Use personal credentials as created in DOOR to log in)

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

Documentation

In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.

 

Download overview leaflet (PDF).

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.

Publications

Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.