Validation of the research methodology and its results is a fundamental element of the
process of scholarly endeavor. Approaches used for construction engineering and …

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

The goal of this fifth edition of Simulation Modeling and Analysis remains the same as that
for the first four editions: to give a comprehensive and state-of-the-art treatment of all the …

Non-uniform random variate generation is an established research area in the intersection
of mathematics, statistics and computer science. Although random variate generation with …

This paper investigates the application of stochastic dynamic programming to the
optimization of charging and frequency regulation capacity bids of an electric vehicle (EV) in …

We consider the problem of deriving confidence intervals for the mean response of a system
that is represented by a stochastic simulation whose parametric input models have been …

Simulation output clearly depends on the form of the input distributions used to drive the
model. Often these input distributions are fitted using finite samples of real-world data. The …

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

“Input uncertainty” refers to the (often unmeasured) effect of not knowing the true, correct
distributions of the basic stochastic processes that drive the simulation. These include, for …

Input uncertainty refers to errors caused by a lack of complete knowledge about the
probability distributions used to generate input variates in stochastic simulation. The …