Stochastic Online Scheduling on Parallel Machines

N. Megow*, M.J. Uetz, T. Vredeveld

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize the weighted completion times of jobs. In contrast to the classical stochastic model where jobs with their processing time distributions are known beforehand, we assume that jobs appear one by one, and every job must be assigned to a machine online. We propose a simple online scheduling policy for that model, and prove a performance guarantee that matches the currently best known performance guarantee for stochastic parallel machine scheduling. For the more general model with job release dates we derive an analogous result, and for nbue distributed processing times we even improve upon the previously best known performance guarantee for stochastic parallel machine scheduling. Moreover, we derive some lower bounds on approximation.
Original languageEnglish
Title of host publicationApproximation and Online Algorithms. WAOA 2004
EditorsG Persiano, R Solis-Oba
PublisherSpringer, Berlin, Heidelberg
Number of pages14
ISBN (Electronic)978-3-540-31833-0
ISBN (Print)978-3-540-24574-2
Publication statusPublished - 1 Jan 2005

Publication series

SeriesLecture Notes in Computer Science

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