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 …

To attain competitive advantages and promote environmental performance, a proactive
approach called green supply chain management (GSCM), has been extensively employed …

In many real‐world applications, designs can only be evaluated pairwise, relative to each
other. Nevertheless, in the simulation literature, almost all the ranking and selection …

Stochastic optimization problems exist widely in many manufacturing and service systems.
Due to the stochastic nature, these problems usually have no analytical solutions and are …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

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 …

In this research, we consider the ranking and selection problem in the presence of
covariates. It is an important problem in personalized decision making. The performance of …

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 …