Stochastic Online Scheduling Revisited

Authors:
Andreas S. Schulz
Affiliations:
Sloan School of Management, Massachusetts Institute of Technology, , Cambridge, USA MA 02139
Venue:
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
Year:
2008

Citing 17
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Abstract

We consider the problem of minimizing the total weighted completion time on identical parallel machines when jobs have stochastic processing times and may arrive over time. We give randomized as well as deterministic online and off-line algorithms that have the best known performance guarantees in either setting, deterministic and off-line or randomized and online. Our analysis is based on a novel linear programming relaxation for stochastic scheduling problems, which can be solved online.