Metamodel-based simulation optimization: A systematic literature review
JVS do Amaral, JAB Montevechi… - … Modelling Practice and …, 2022 - Elsevier
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 …
attention for their ability to represent and improve complex systems. The use of …
Adaptive metamodeling simulation optimization: Insights, challenges, and perspectives
JVS do Amaral, JAB Montevechi… - Applied Soft …, 2024 - Elsevier
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 …
several fields, enabling system evaluation under varying conditions, facilitating performance …
Scalable adaptive batch sampling in simulation-based design with heteroscedastic noise
A van Beek, UF Ghumman… - Journal of …, 2021 - asmedigitalcollection.asme.org
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) …
models with spatially varying noise. The proposed scheme has two primary advantages:(i) …
Metamodeling-based simulation optimization in manufacturing problems: a comparative study
JVS do Amaral, R de Carvalho Miranda… - … International Journal of …, 2022 - Springer
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 …
allowing decision-makers to allocate their resources more assertively and deal with complex …
Part-x: A family of stochastic algorithms for search-based test generation with probabilistic guarantees
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 …
practical and effective method for discovering erroneous behaviors in Cyber-Physical …
Probabilistic branch and bound considering stochastic constraints
H Huang, SC Tsai, C Park - European Journal of Operational Research, 2024 - Elsevier
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; …
(i) the inability to evaluate the objective and multiple constraint functions analytically; …
Combined global and local search for optimization with gaussian process models
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 …
machine learning. In general, it first estimates a GP model based on a few observations from …
A multilevel simulation optimization approach for quantile functions
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 …
variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of …
Data envelopment analysis for algorithm efficiency assessment in metamodel-based simulation optimization
JVS do Amaral, R de Carvalho Miranda… - … International Journal of …, 2022 - Springer
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 …
industrial problems stood out as a promising research field, mainly due to the advance of …
A nonparametric Bayesian approach for simulation optimization with input uncertainty
Stochastic simulation models are increasingly popular for analyzing complex stochastic
systems. However, the input distributions required to drive the simulation are typically …
systems. However, the input distributions required to drive the simulation are typically …