Stochastic Discrete-Event Simulation — Probabilistic Modeling of Event-Driven Systems

Use a pilot study of 10–30 replications to estimate variance, then apply the half-width formula n = (z_{α/2} s / h)² to find the minimum R needed for the desired confidence interval half-width h. Thirty or more replications is a common practical minimum.

Monte Carlo Simulation samples from distributions to estimate a static quantity (e.g., a risk score or integral). Stochastic DES also samples from distributions but does so dynamically as the simulation clock advances, capturing time-ordered event interactions, queues, and resource contention that Monte Carlo cannot represent.

Plot the running mean of a key output metric over time across replications. Use Welch's method to identify where the mean stabilizes and delete observations before that point. Alternatively, start replications in a typical steady-state configuration to reduce initialization bias.

Choose ABM when individual agent behavior, learning, adaptation, or emergent collective phenomena are the focus. Choose Stochastic DES when the emphasis is on resource flows, queues, and event timing in a system whose logic is well-defined and data-rich.

Yes. Simulation-optimization couples DES with metaheuristics (genetic algorithms, simulated annealing) or response-surface methods to search for configurations that minimize or maximize a stochastic performance measure, though it is computationally expensive.