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

Ordinal Optimization has emerged as an efficient technique for simulation and optimization.
Exponential convergence rates can be achieved in many cases. In this paper, we present a …

Many simulation experiments require much computer time, so they necessitate interpolation
for sensitivity analysis and optimization. The interpolating functions are'metamodels'(or' …

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 …

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 …

In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox
shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve …

Subjective probability and Bayesian methods provide a unified approach to handle not only
randomness from stochastic sample-paths, but also uncertainty about input parameters and …

Several major risk studies have been performed in recent years in the maritime
transportation domain. These studies have had significant impact on management practices …

Ordinal optimization has emerged as an efficient technique for simulation and optimization.
Exponential convergence rates can be achieved in many cases. In this paper, we present a …