Under a Bayesian framework, we formulate the fully sequential sampling and selection
decision in statistical ranking and selection as a stochastic control problem, and derive the …

Abstract The Winter Simulation Conference serves as the initial publication venue for many
advances in ranking and selection (R&S), including the recently developed R&S procedures …

This paper considers the problem of selecting the top-m designs using simulation with input
uncertainty. The performance of each design is measured by its worst case performance …

We review simulation optimization methods and discuss how these methods underpin
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …

This paper is concerned with the efficient estimation of the risk measure of a system where
the estimation requires solving a two-stage simulation optimization problem. The first stage …

In this note, we consider the statistical ranking and selection problem of finding the best
alternative when the performances of each alternative must be estimated by sampling. We …

We consider a context-dependent ranking and selection problem. The best design is not
universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic …

Simulation is often used to estimate the performance of alternative system designs for
selecting the best. For a complex system, high-fidelity simulation is usually time-consuming …

We review static and dynamic optimization formulations for simulation allocation and
selection procedures and revisit several sampling approaches under a single umbrella. We …

We consider best arm identification in the multiarmed bandit problem. Assuming certain
continuity conditions of the prior, we characterize the rate of the Bayesian simple regret …