direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Logo der TU Berlin

Inhalt des Dokuments

Preprint 03-2005

Combinatorial Optimization & Graph Algorithms group (COGA-Preprints)

Models and Algorithms for Stochastic Online Scheduling
not available
stochastic scheduling, online scheduling, average completion time, policies
We introduce a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to traditional stochastic scheduling models, we assume that jobs arrive online, and there is no knowledge about the jobs that will arrive in the future. The model incorporates both, stochastic scheduling and online scheduling as a special case. The particular setting we analyze is nonpreemptive parallel machine scheduling, with the objective to minimize the total weighted completion times of jobs. We propose simple, combinatorial online scheduling policies for that model, and derive performance guarantees that match the currently best known performance guarantees for stochastic and online parallel machine scheduling. For processing times that follow NBUE distributions, we improve upon previously best known performance bounds from stochastic scheduling, even though we consider a more general setting.
Download as [PDF]
Title: Source

Zusatzinformationen / Extras


Schnellnavigation zur Seite über Nummerneingabe