Abstract
Motivated by our study of a general stochastic scheduling optimization problem,’ we examine various static instances where the stochastic problems reduce to the corresponding deterministic scheduling problems without loss of generality. Following our solution approach for these special cases leads us to rederive many known results in a fairly elegant manner, yielding simple approximation algorithms whose guarantees are shown to match the best known results. We believe that improvements in the approximation guarantees for some of these special cases will be possible by exploiting the convex programming techniques of our approach. We formulate the nonlinear scheduling optimization problem, we provide a convex relaxation, and then we present a simple (randomized) rounding scheme. The interested reader is referred to our technical report? for additional details, including proofs and references.
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