We study the problem of scheduling a set of J jobs on M machines with stochastic job processing times when no preemptions are allowed and with a weighted sum of expected completion times objective.

We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, ...