Stochastic online scheduling on parallel machines

G Persiano (Editor), N. Megow, Marc Jochen Uetz, R Solis-Oba (Editor), T. Vredeveld

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

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 languageUndefined
Pages167-180
Number of pages14
DOIs
Publication statusPublished - Feb 2005

Keywords

  • EWI-12127
  • IR-62219

Cite this

Persiano, G. (Ed.), Megow, N., Uetz, M. J., Solis-Oba, R. (Ed.), & Vredeveld, T. (2005). Stochastic online scheduling on parallel machines. 167-180. https://doi.org/10.1007/11389811_15