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

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 …

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

Discrete-event simulation models generate random variates from input distributions and
compute outputs according to the simulation logic. The input distributions are typically fitted …

Recently, a stochastic data-driven framework was introduced for forecasting uncertain
multiscale hydrological and water resources processes (eg, streamflow, urban water …

This introduction recalls the considerable socio-economic challenges associated with
extreme natural hazards. The possibilities of statistical quantification of past hazards and …

This study aims to investigate the influence of health self-identity, self-efficacy beliefs, and
subjective norms on healthy lifestyles and, in turn, behavioural intentions. Health attitude is …

In stochastic simulation, input modeling refers to the process of identifying and selecting the
probability distributions, called input models, from which are generated the random variates …

In stochastic simulation, input uncertainty refers to the output variability arising from the
statistical noise in specifying the input models. This uncertainty can be measured by a …

Recent advances in sensing, data analytics, and manufacturing technologies (eg, 3-D
printing, soft robotics, nanotechnologies, etc.) provide the potential to produce highly …