Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

Metamodels as cheap approximation models for expensive to evaluate functions have been
commonly used in simulation optimization problems. Among various types of metamodels …

We consider simulation optimization in the presence of input uncertainty. In particular, we
assume that the input distribution can be described by some continuous parameters, and …

We present ARGA–Adaptive Robust Genetic Algorithm–to optimize zero-one simulation
problems by incorporating input uncertainty. In ARGA, a surviving population of solutions …

Robust ranking and selection (R&S) is an important and challenging variation of
conventional R&S that seeks to select the best alternative among a finite set of alternatives. It …

Robust selection of the best (RSB) is an important problem in the simulation area, when
there exists input uncertainty in the underlying simulation model. RSB models this input …

Feature Selection (FS) is one of the essential steps in any predictive machine learning (ML)
problem. FS aims to identify the most informative and contributing features (ie, variables) …

Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

ABSTRACT We present ARGA–Adaptive Robust Genetic Algorithm–to optimize simulation
problems with binary variables affected by input uncertainty and Monte Carlo noise. In this …