Finding the pareto set for multi-objective simulation models by minimization of expected opportunity cost

In this study, we mainly explore how to optimally allocate the computing budget for a multi-objective ranking and selection (MORS) problem when the measure of selection quality is the expected opportunity cost (OC). We define OC incurred to both the observed Pareto and non-Pareto set, and present a sequential procedure to allocate the replications among the designs according to some asymptotic allocation rules. Numerical analysis shows that the proposed solution framework works well when compared with other algorithms in terms of its capability of identifying the true Pareto set.