In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …

Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

Input uncertainty refers to errors caused by a lack of complete knowledge about the
probability distributions used to generate input variates in stochastic simulation. The …

In this paper, we consider the ranking and selection (R&S) problem with input uncertainty. It
seeks to maximize the probability of correct selection (PCS) for the best design under a fixed …

When input distributions to a simulation model are estimated from real-world data, they
naturally have estimation error causing input uncertainty in the simulation output. If an …

Existing research in ranking and selection has focused on the problem of selecting the best
design, subset selection and selecting the set of Pareto designs. Few works have addressed …

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 …

Specifying a proper input distribution is often a challenging task in simulation modeling. In
practice, there may be multiple plausible distributions that can fit the input data reasonably …

Any performance analysis based on stochastic simulation is subject to the errors inherent in
misspecifying the modeling assumptions, particularly the input distributions. In situations …

Metamodels as cheap approximation models for expensive to evaluate functions have been
commonly used in simulation optimization problems. Among various types of metamodels …