Input processes driving a simulation are random variables (e.g.,interarrival times, service times, and breakdown times). Must regard the output from the simulation as random. Runs of the simulation only yield estimates of measures of system performance (e.g., the mean customer waiting time). These estimators are themselves random variables, and are therefore subject to sampling error. Must take sampling error must be taken into account to make valid inferences concerning system performance.
Problem: simulations almost never produce raw output that is independent and identically distributed (i.i.d.) normal data. Example: Customer waiting times from a queueing system. . .
(1) Are not independent — typically, they are serially correlated. If one customer at the post office waits in line a long time, then the next customer is also likely to wait a long time.
(2) Are not identically distributed. Customers showing up early in the morning might have a much shorter wait than those who show up just before closing time.
(3) Are not normally distributed — they are usually skewed to the right (and are certainly never less than zero).
Thus, it’s difficult to apply “classical” statistical techniques to the analysis of simulation output.Our purpose: Give methods to perform statistical analysis ofoutput from discrete-event computer simulations.
Types of Simulations
To facilitate the presentation, we identify two types of simulations with respect to output analysis: Finite-Horizon (Terminating) and Steady-State simulations.
Finite-Horizon Simulations: The termination of a finite-horizon simulation takes place at a specific time or is caused by the occurrence of a specific event. Examples are:
- Mass transit system between during rush hour.
- Distribution system over one month.
- Production system until a set of machines breaks down.
- Start-up phase of any system — stationary or nonstationary
Steady-state simulations: The purpose of a steady-state simulation is the study of the long-run behavior of a system. A performance measure is called a steady-state parameter if it is a characteristic of the equilibrium distribution of an output stochastic process. Examples are: Continuously operating communication system where the objective is the computation of the mean delay of a packet in the long run. Distribution system over a long period of time.
here I attach the course video of Simulation Output Analysis to provide more information