Selecting the best system: a decision-theoretic approach
Proceedings of the 29th conference on Winter simulation
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Selecting a Selection Procedure
Management Science
Economic Analysis of Simulation Selection Problems
Management Science
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Performance measures for ranking and selection procedures
Proceedings of the Winter Simulation Conference
A Bayesian approach to stochastic root finding
Proceedings of the Winter Simulation Conference
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For many discrete simulation optimization applications, it is often difficult to decide which Ranking and Selection (R&S) procedure to use. To efficiently compare R&S procedures, we present a three-layer performance evaluation process. We show that the two most popular performance formulations, namely the Bayesian formulation and the indifference zone formulation, have a common representation analogous to convex risk measures used in mathematical finance. We then specify how a decision maker can impose a performance requirement on R&S procedures that is more adequate for her risk attitude than the indifference zone or the Bayesian performance requirements. Such a performance requirement partitions the space of R&S procedures into acceptable and nonacceptable procedures. The minimal computational budget required for a procedure to become acceptable introduces an easy-to-interpret preference order on the set of R&S policies. We demonstrate with a numerical example how the introduced framework can be used to guide the choice of selection procedure in practice.