An improved averaged two-replication procedure with Latin hypercube sampling

The averaged two-replication procedure assesses the quality of a candidate solution to a stochastic program by forming point and confidence interval estimators on its optimality gap. We present an improved averaged two-replication procedure that uses Latin hypercube sampling to form confidence intervals of optimality gap. This new procedure produces tighter and less variable interval widths by reducing the sampling error by 2. Despite having tighter intervals, it improves an earlier procedure's asymptotic coverage probability bound from (1−α)2 to (1−α).