Reusing Search Data in Ranking and Selection

Abstract

It is tempting to reuse replications taken during a simulation optimization search as input to a ranking-and-selection procedure. However, even when the random inputs used to generate replications are identically distributed and independent within and across systems, we show that for searches that use the observed performance of explored systems to identify new systems, the replications are conditionally dependent given the sequence of returned systems. Through simulation experiments, we demonstrate that reusing the replications taken during search in selection and subset-selection procedures can result in probabilities of correct and good selection well below the guaranteed levels. Based on these negative findings, we call into question the guarantees of established ranking-and-selection procedures that reuse search data. We also rigorously define guarantees for ranking-and-selection procedures after search and discuss how procedures that only provide guarantees in the preference zone are ill-suited to this setting.