Selection-of-the-best procedures for optimization via simulation

We propose fully sequential indifference-zone selection procedures that are specifically for use within an optimization-via-simulation algorithm when simulation is costly and partial or complete information on solutions previously visited is maintained. Sequential Selection with Memory guarantees to select the best or near-best alternative with a user-specified probability when some solutions have already been sampled and their previous samples are retained. For the case when only summary information is retained, we derive a modified procedure. We illustrate how our procedure can be applied to optimization-via-simulation problems and compare its performance with other methods by numerical examples