A sequential procedure for neighborhood selection-of-the-best in optimization via simulation

We propose a fully sequential indifference-zone selection procedure that is 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, their previous samples are retained, and simulation outputs are i.i.d. normal. For the case when only summary information on solutions is retained, we derive a modified procedure. We illustrate how our procedures can be applied to optimization-via-simulation problems and compare its performance with other methods by numerical examples.