SELECTING THE BEST SYSTEM IN STEADY-STATE SIMULATIONS USING BATCH MEANS

摘要

Suppose that we want to compare A; different systems, where μ_i denotes the steady-state mean performance of system i. Our goal is to use simulation to pick the "best" system (i.e., the one with the largest or smallest steady-state mean). To do this, we present some two-stage procedures based on the method of batch means. Our procedures also construct multiple-comparisons-with-the-best (MCB) confidence intervals for μ_i - max_ j ≠ i μ_j, i = 1,..., k. Under the assumption of an indifference zone of (absolute or relative) width δ, we can show that asymptotically (as δ → 0 with the size of the batches proportional to 1/δ~2), the joint probability of correctly selecting the best system and of the MCB confidence intervals simultaneously containing μ_i - max_j ≠ i μ_j, i = 1,..., k, is at least 1 - α, where α is prespecified by the user.