Discrete event simulation

A discrete-event simulation (DES) models the operation of a system as a discrete sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Between consecutive events, no change in the system is assumed to occur; thus the simulation can directly jump in time from one event to the next.

This contrasts with continuous simulation in which the simulation continuously tracks the system dynamics over time. Instead of being event-based, this is called an activity-based simulation; time is broken up into small time slices and the system state is updated according to the set of activities happening in the time slice. Because discrete-event simulations do not have to simulate every time slice, they can typically run much faster than the corresponding continuous simulation.

A more recent method is the three-phased approach to discrete event simulation (Pidd, 1998). In this approach, the first phase is to jump to the next chronological event. The second phase is to execute all events that unconditionally occur at that time (these are called B-events). The third phase is to execute all events that conditionally occur at that time (these are called C-events). The three phase approach is a refinement of the event-based approach in which simultaneous events are ordered so as to make the most efficient use of computer resources. The three-phase approach is used by a number of commercial simulation software packages, but from the user's point of view, the specifics of the underlying simulation method are generally hidden.

A common exercise in learning how to build discrete-event simulations is to model a queue, such as customers arriving at a bank to be served by a teller. In this example, the system entities are Customer-queue and Tellers. The system events are Customer-Arrival and Customer-Departure. (The event of Teller-Begins-Service can be part of the logic of the arrival and departure events.) The system states, which are changed by these events, are Number-of-Customers-in-the-Queue (an integer from 0 to n) and Teller-Status (busy or idle). The random variables that need to be characterized to model this system are Customer-Interarrival-Time and Teller-Service-Time. An agent-based framework for performance modeling of an optimistic parallel discrete event simulator is another example for a discrete event simulation.

...
Wikipedia