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

A number of authors have identified problematic issues with techniques used in current
simulation practice for selecting probability distributions and their parameters for input to …

When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …

Simulations are often driven by input models estimated from finite real-world data. When we
use simulations to assess the performance of a stochastic system, there exist two sources of …

When we use simulation to estimate the performance of a stochastic system, the input
models that drive the simulation are often estimated by a finite sample of real-world data …

Increased computer speed and memory have encouraged simulation analysts to develop
ever more realistic stochastic models. Despite these advancements in computing hardware …

This paper provides an overview of the five most commonly used statistical techniques for
improving the efficiency of stochastic simulations: control variates, common random …

We introduce the topic of this book, explain what we mean by stochastic computer simulation
and provide examples of application areas. We motivate the remaining chapters in the book …

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

Transportation research has grown to transcend disciplinary boundaries and become a
substantial multidisciplinary and interdisciplinary field of study. Its growing sophistication …