Recent advances in simulation optimization research and explosive growth in computing
power have made it possible to optimize complex stochastic systems that are otherwise …

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 …

Contextual decision-making problems have witnessed extensive applications in various
fields such as online content recommendation, personalized healthcare, and autonomous …

Selecting the optimal subset is highly beneficial to numerous developments in simulation
optimization. This paper studies the problem of maximizing the probability of correctly …

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 formulate the statistical selection problem in a general dynamic framework comprising
fully sequential sampling allocation and optimal design selection. Because the traditional …

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

This research considers the problem of ranking the top simulated designs in the presence of
stochastic constraints. The objective and constraint measures of each design must be …

Motivated by the practical needs in simulation optimization, this paper considers the problem
of selecting the best m and worst n designs from a total of k alternatives based on their mean …

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 …