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

Digital twins and shadows have gained increasing popularity in industry and research. The
terms describe simulation systems that mirror real-world systems, such as service or …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

Nested simulation concerns estimating functionals of a conditional expectation via
simulation. In this paper, we propose a new method based on kernel ridge regression to …

We consider stochastic optimization under distributional uncertainty, where the unknown
distributional parameter is estimated from streaming data that arrive sequentially over time …

Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …

Nested simulation is typically used to estimate the functional of a conditional expectation.
Considerable research has been performed on point estimation for various functionals …

Bootstrapping is a popular tool for quantifying input uncertainty, inflated uncertainty in the
simulation output caused by finite-sample estimation error in the input models. Typical …

We consider a simulation-based ranking and selection (R&S) problem with input uncertainty,
in which unknown input distributions can be estimated using input data arriving in batches of …

We consider simulation optimization under input uncertainty, where the unknown input
parameter is estimated from streaming data arriving in batches over time. Moreover, data …