Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling

Abstract

We consider the problem of selecting the best feasible system with constraints on multiple secondary performance measures. We develop fully sequential indifference-zone procedures to solve this problem that guarantee a nominal probability of correct selection. In addition, we address two issues critical to the efficiency of these procedures: namely, the allocation of error between feasibility determination and selection of the best system, and the use of Common Random Numbers. We provide a recommended error allocation as a function of the number of constraints, supported by an experimental study and an approximate asymptotic analysis. The validity and efficiency of the new procedures with independent and CRN are demonstrated through both analytical and experimental results.