A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
Selecting a Selection Procedure
Management Science
Economic Analysis of Simulation Selection Problems
Management Science
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
A brief introduction to optimization via simulation
Winter Simulation Conference
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Theory and Applications of Robust Optimization
SIAM Review
Performance measures for ranking and selection procedures
Proceedings of the Winter Simulation Conference
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The objective of ranking and selection is to efficiently allocate an information budget among a set of design alternatives with unknown values in order to maximize the decision-maker's chances of discovering the best alternative. The field of robust optimization, however, considers risk-averse decision makers who may accept a suboptimal alternative in order to minimize the risk of a worst-case outcome. We bring these two fields together by defining a Bayesian ranking and selection problem with a robust implementation decision. We propose a new simulation allocation procedure that is risk-neutral with respect to simulation outcomes, but risk-averse with respect to the implementation decision. We discuss the properties of the procedure and present numerical examples illustrating the difference between the risk-averse problem and the more typical risk-neutral problem from the literature.