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Models and Algorithms for Stochastic Online Scheduling1 Nicole Megow
 

Summary: Models and Algorithms for Stochastic Online Scheduling1
Nicole Megow
Technische Universit¨at Berlin, Institut f¨ur Mathematik, Strasse des 17. Juni 136, 10623 Berlin, Germany.
email: nmegow@math.tu-berlin.de
Marc Uetz
Maastricht University, Department of Quantitative Economics, P.O. Box 616, 6200 MD Maastricht, The
Netherlands.
email: m.uetz@ke.unimaas.nl
Tjark Vredeveld2
Maastricht University, Department of Quantitative Economics, P.O. Box 616, 6200 MD Maastricht, The
Netherlands.
email: t.vredeveld@ke.unimaas.nl
We consider 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 consider is non-preemptive parallel machine schedul-
ing, with the objective to minimize the total weighted completion times of jobs. We analyze simple, combinatorial
online scheduling policies for that model, and derive performance guarantees that match performance guaran-
tees previously known for stochastic and online parallel machine scheduling, respectively. For processing times

  

Source: Al Hanbali, Ahmad - Department of Applied Mathematics, Universiteit Twente

 

Collections: Engineering