Dagstuhl-Seminar 23061
Scheduling
( 05. Feb – 10. Feb, 2023 )
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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)
Kontakt
- Michael Gerke (für wissenschaftliche Fragen)
- Christina Schwarz (für administrative Fragen)
Dagstuhl Reports
As part of the mandatory documentation, participants are asked to submit their talk abstracts, working group results, etc. for publication in our series Dagstuhl Reports via the Dagstuhl Reports Submission System.
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Dagstuhl Seminar Wiki
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Gemeinsame Dokumente
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
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:
- Scheduling with error-prone learned predictions
- Data-driven algorithm design
- 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.
Creative Commons BY 4.0
Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, and Sergei Vassilvitskii
- Antonios Antoniadis (University of Twente, NL) [dblp]
- Yossi Azar (Tel Aviv University, IL) [dblp]
- Etienne Bamas (EPFL - Lausanne, CH)
- Siddhartha Banerjee (Cornell University - Ithaca, US)
- Sanjoy Baruah (Washington University - St. Louis, US) [dblp]
- Sami Davies (Northwestern University - Evanston, US)
- Christoph Dürr (Sorbonne University - Paris, FR) [dblp]
- Franziska Eberle (London School of Economics, GB) [dblp]
- Daniel Freund (MIT - Camridge, US)
- Naveen Garg (Indian Institute of Technology - New Dehli, IN) [dblp]
- Vineet Goyal (Columbia University, US)
- Thomas Kesselheim (Universität Bonn, DE) [dblp]
- Samir Khuller (Northwestern University - Evanston, US) [dblp]
- Amit Kumar (Indian Institute of Technology - New Dehli, IN) [dblp]
- Silvio Lattanzi (Google - Barcelona, ES) [dblp]
- Thomas Lavastida (University of Texas - Dallas, US)
- Alex Lindermayr (Universität Bremen, DE)
- Alberto Marchetti-Spaccamela (Sapienza University of Rome, IT) [dblp]
- Claire Mathieu (CNRS - Paris, FR) [dblp]
- Nicole Megow (Universität Bremen, DE) [dblp]
- Benjamin J. Moseley (Carnegie Mellon University - Pittsburgh, US) [dblp]
- Seffi Naor (Technion - Haifa, IL) [dblp]
- Debmalya Panigrahi (Duke University - Durham, US)
- Kirk Pruhs (University of Pittsburgh, US) [dblp]
- Lars Rohwedder (Maastricht University, NL) [dblp]
- Thomas Rothvoss (University of Washington - Seattle, US) [dblp]
- Kevin Schewior (University of Southern Denmark - Odense, DK) [dblp]
- Jens Schlöter (Universität Bremen, DE)
- Jiri Sgall (Charles University - Prague, CZ) [dblp]
- David Shmoys (Cornell University - Ithaca, US) [dblp]
- Martin Skutella (TU Berlin, DE) [dblp]
- Frits C. R. Spieksma (TU Eindhoven, NL) [dblp]
- Clifford Stein (Columbia University, US) [dblp]
- Leen Stougie (CWI - Amsterdam, NL) [dblp]
- Ola Svensson (EPFL - Lausanne, CH) [dblp]
- Chaitanya Swamy (University of Waterloo, CA)
- Marc Uetz (University of Twente - Enschede, NL) [dblp]
- Ali Vakilian (TTIC - Chicago, US)
- Sergei Vassilvitskii (Google - New York, US) [dblp]
- Jose Verschae (PUC - Santiago de Chile, CL) [dblp]
- Tjark Vredeveld (Maastricht Univ. School of Business & Economics, NL) [dblp]
- Andreas Wiese (TU München, DE) [dblp]
- Rudy Zhou (Carnegie Mellon University - Pittsburgh, US)
Verwandte Seminare
- Dagstuhl-Seminar 08071: Scheduling (2008-02-10 - 2008-02-15) (Details)
- Dagstuhl-Seminar 10071: Scheduling (2010-02-14 - 2010-02-19) (Details)
- Dagstuhl-Seminar 13111: Scheduling (2013-03-10 - 2013-03-15) (Details)
- Dagstuhl-Seminar 16081: Scheduling (2016-02-21 - 2016-02-26) (Details)
- Dagstuhl-Seminar 18101: Scheduling (2018-03-04 - 2018-03-09) (Details)
- Dagstuhl-Seminar 20081: Scheduling (2020-02-16 - 2020-02-21) (Details)
Klassifikation
- Data Structures and Algorithms
- Machine Learning
Schlagworte
- scheduling
- mathematical optimization
- approximation algorithms
- learning methods
- uncertainty
