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 …

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 …

Monte Carlo tree search (MCTS) is a general approach to solving game problems, playing a
central role in Google DeepMind's AlphaZero and its predecessor AlphaGo, which famously …

We present a methodology for an artificial neural network (ANN) based metamodeling of
simulation models in the special case when online decision making routines are invoked …

Tabu search (TS) is a powerful method for solving combinatorial optimization problems.
However, when TS is adopted for stochastic simulation optimization, the simulation noises …

On one hand, large-scale ranking and selection (R&S) problems require a large amount of
computation. On the other hand, parallel computing environments that provide a large …

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

We consider the problem of optimizing the performance of a stochastic system, eg, a discrete-
event system, where the system performance is evaluated using stochastic simulations. Our …

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 …