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

Simulation is an established tool for predicting and evaluating the performance of complex
stochastic systems that are analytically intractable. Recent research in simulation …

Simulation optimization can be used to solve many complex optimization problems in
automation applications such as job scheduling and inventory control. We propose a new …

In this article, we study the strategy optimization problem of black-box range gate pull-off
(RGPO) jamming. In the black-box RGPO jamming, the jammer does not have extensive …

Particle swarm optimization (PSO) is a popular metaheuristic for deterministic optimization.
Originated in the interpretations of the movement of individuals in a bird flock or fish school …

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

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 …

Federated learning is a state-of-the-art technology used in the fog computing, which allows
distributed learning to train cross-device data while achieving efficient performance. Many …

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 …

Simulation models of different fidelity levels are often available for a complex system. High-
fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to …