Over the past few decades, modeling, simulation, and optimization tools have received
attention for their ability to represent and improve complex systems. The use of …

Abstract A pillar of Industry 4.0, Simulation Optimization is a powerful tool used across
several fields, enabling system evaluation under varying conditions, facilitating performance …

In this study, we propose a scalable batch sampling scheme for optimization of simulation
models with spatially varying noise. The proposed scheme has two primary advantages:(i) …

In the context of modern industry, optimization emerges as one of the most powerful tools,
allowing decision-makers to allocate their resources more assertively and deal with complex …

Requirements driven search-based testing (also known as falsification) has proven to be a
practical and effective method for discovering erroneous behaviors in Cyber-Physical …

In this paper, we investigate a simulation optimization problem that poses challenges due to
(i) the inability to evaluate the objective and multiple constraint functions analytically; …

Gaussian process (GP) model based optimization is widely applied in simulation and
machine learning. In general, it first estimates a GP model based on a few observations from …

A quantile is a popular performance measure for a stochastic system to evaluate its
variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of …

In the last years, the use of metamodel-based simulation optimization techniques to solve
industrial problems stood out as a promising research field, mainly due to the advance of …

Stochastic simulation models are increasingly popular for analyzing complex stochastic
systems. However, the input distributions required to drive the simulation are typically …