We consider the problem of finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of stochastic constraints. When the constraints are subjective, a decision maker may want to test multiple threshold values for the constraints. Or the decision maker may simply want to determine how a set of feasible systems changes as constraints become more strict with the objective of pruning systems or finding the system with the best performance. When only the constraint thresholds change for the same set of underlying systems, it is natural to reuse observations collected from the feasibility check with a different threshold value. We present an indifference-zone procedure that recycles observations and provide an overall probability of correct decision for all threshold values. Our numerical experiments show that the proposed procedure performs well in reducing the required number of observations while providing a statistical guarantee on the probability of correct decision. Summary of Contribution: We consider the problem of determining the feasibility of a finite number of systems in the presence of subjective constraints on performance measures that can be estimated only by stochastic simulation. Specifically, our work focuses on the situation where the decision maker is willing to relax some constraint thresholds if necessary to achieve feasibility. Also, we discuss how our proposed procedures can help select the best system in the presence of multiple objectives. This is the first work that considers subjective constraints in the field of ranking and selection in simulation and it provides a practically useful decision-making tool with more flexibility on feasibility determination. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.1227 .

We present and evaluate three ranking-and-selection procedures for use in steady-state simulation experiments when the goal is to find which among a finite number of alternative systems has the largest or smallest long-run average performance. All three procedures extend existing methods for independent and identically normally distributed observations to general stationary output processes, and all procedures are sequential. We also provide our thoughts about the evaluation of simulation design and analysis procedures, and illustrate these concepts in our evaluation of the new procedures.