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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05031.18
URN: urn:nbn:de:0030-drops-1931
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2005/193/
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Schulz, Andreas S.
New Old Algorithms for Stochastic Scheduling
Abstract
We consider the stochastic identical parallel machine scheduling problem and its online extension, when the objective is to minimize the expected total weighted completion time of a set of jobs that are released over time. We give randomized as well as deterministic online and offline algorithms that have the best known performance guarantees in either setting, online or offline and deterministic or randomized. Our analysis is based on a novel linear programming relaxation for stochastic scheduling problems that can be solved online.
BibTeX - Entry
@InProceedings{schulz:DagSemProc.05031.18,
author = {Schulz, Andreas S.},
title = {{New Old Algorithms for Stochastic Scheduling}},
booktitle = {Algorithms for Optimization with Incomplete Information},
pages = {1--9},
series = {Dagstuhl Seminar Proceedings (DagSemProc)},
ISSN = {1862-4405},
year = {2005},
volume = {5031},
editor = {Susanne Albers and Rolf H. M\"{o}hring and Georg Ch. Pflug and R\"{u}diger Schultz},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2005/193},
URN = {urn:nbn:de:0030-drops-1931},
doi = {10.4230/DagSemProc.05031.18},
annote = {Keywords: stochastic scheduling , online algorithms , competitive analysis , approximation algorithms , linear programming relaxations}
}
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Keywords: |
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stochastic scheduling , online algorithms , competitive analysis , approximation algorithms , linear programming relaxations |
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Collection: |
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05031 - Algorithms for Optimization with Incomplete Information |
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Issue Date: |
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2005 |
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Date of publication: |
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17.06.2005 |
