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TU Berlin

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Sven Jäger

Lupe

Research assistant

Fakultät II - Mathematik und Naturwissenschaften
Institut für Mathematik, Sekr. MA 5-2

Technische Universität Berlin
Straße des 17. Juni 136
10623 Berlin
Germany

E-Mail:
Tel.: +49 (0)30 314-21095
Fax: +49 (0)30 314-25191
Room: MA 511
Consultation hours: Tuesday 5:00 pm. Please send me an email before Tuesday noon in order to get the zoom link.

Research interests

  • Apprroximation algorithms
  • Scheduling
  • Symmetric Chain Decompositions

Teaching

Assistances
Summer 2021
Computerorientierte Mathematik II (theoretical exercises)
Winter 2020/21
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2020
Discrete Optimization (ADM II)
Winter 2019/20
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2019
Discrete Optimization (ADM II)
Winter 2018/19
Introduction to Linear and Combinatorial Optimization (ADM I)
Summer 2018
Computerorientierte Mathematik II (programming exercises)
Winter 2017/18
Computerorientierte Mathematik I (programming exercises)
Summer 2017
Computerorientierte Mathematik II (theoretical exercises)
Winter 2016/17
Computerorientierte Mathematik I (theoretical exercises)

Curriculum Vitae

Since July 2016
Doctoral student with Martin Skutella, Technische Universität Berlin
2013 - 2016:
Master studies in Mathematics and in Applied Computer Science at Georg-August-Universität Göttingen, Germany.
2010 - 2013:
Bachelor studies in Mathematics and in Applied Computer Science at Georg-August-Universität Göttingen, Germany.
2010:
Abitur at Hainberg-Gymnasium Göttingen

Publications

Generalizing the Kawaguchi-Kyan Bound to Stochastic Parallel Machine Scheduling
Citation key JaegerSkutella2018
Author Jäger, Sven and Skutella, Martin
Title of Book 35th Symposium on Theoretical Aspects of Computer Science (STACS 2018)
Pages 43:1–43:14
Year 2018
ISBN 978-3-95977-062-0
ISSN 1868-8969
DOI 10.4230/LIPIcs.STACS.2018.43
Location Caen
Address Dagstuhl, Germany
Volume 96
Editor Niedermeier, Rolf and Vallée, Brigitte
Publisher Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik
Series Leibniz International Proceedings in Informatics (LIPIcs)
Abstract Minimizing the sum of weighted completion times on m identical parallel machines is one of the most important and classical scheduling problems. For the stochastic variant where processing times of jobs are random variables, Möhring, Schulz, and Uetz (1999) presented the first and still best known approximation result, achieving, for arbitrarily many machines, performance ratio 1+1/2(1+Δ), where Δ is an upper bound on the squared coefficient of variation of the processing times. We prove performance ratio 1+1/2(√2-1)(1+Δ) for the same underlying algorithm–-the Weighted Shortest Expected Processing Time (WSEPT) rule. For the special case of deterministic scheduling (i.e., Δ=0), our bound matches the tight performance ratio 1/2(1+√2) of this algorithm (WSPT rule), derived by Kawaguchi and Kyan in a 1986 landmark paper. We present several further improvements for WSEPT's performance ratio, one of them relying on a carefully refined analysis of WSPT yielding, for every fixed number of machines m, WSPT's exact performance ratio of order 1/2(1+√2)-O(1/m²).
Link to original publication Download Bibtex entry
Generalizing the Kawaguchi-Kyan Bound to Stochastic Parallel Machine Scheduling
Citation key JaegerSkutella2018
Author Jäger, Sven and Skutella, Martin
Title of Book 35th Symposium on Theoretical Aspects of Computer Science (STACS 2018)
Pages 43:1–43:14
Year 2018
ISBN 978-3-95977-062-0
ISSN 1868-8969
DOI 10.4230/LIPIcs.STACS.2018.43
Location Caen
Address Dagstuhl, Germany
Volume 96
Editor Niedermeier, Rolf and Vallée, Brigitte
Publisher Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik
Series Leibniz International Proceedings in Informatics (LIPIcs)
Abstract Minimizing the sum of weighted completion times on m identical parallel machines is one of the most important and classical scheduling problems. For the stochastic variant where processing times of jobs are random variables, Möhring, Schulz, and Uetz (1999) presented the first and still best known approximation result, achieving, for arbitrarily many machines, performance ratio 1+1/2(1+Δ), where Δ is an upper bound on the squared coefficient of variation of the processing times. We prove performance ratio 1+1/2(√2-1)(1+Δ) for the same underlying algorithm–-the Weighted Shortest Expected Processing Time (WSEPT) rule. For the special case of deterministic scheduling (i.e., Δ=0), our bound matches the tight performance ratio 1/2(1+√2) of this algorithm (WSPT rule), derived by Kawaguchi and Kyan in a 1986 landmark paper. We present several further improvements for WSEPT's performance ratio, one of them relying on a carefully refined analysis of WSPT yielding, for every fixed number of machines m, WSPT's exact performance ratio of order 1/2(1+√2)-O(1/m²).
Link to original publication Download Bibtex entry

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