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 time can directly jump to the occurrence time of the next event, which is called next-event time progression. 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 time can directly jump to the occurrence time of the next event, which is called next-event time progression. In addition to next-event time progression, there is also an alternative approach, called fixed-increment time progression, where time is broken up into small time slices and the system state is updated according to the set of events/activities happening in the time slice. Because not every time slice has to be simulated, a next-event time simulation can typically run much faster than a corresponding fixed-increment time simulation. Both forms of DES contrast with continuous simulation in which the system state is changed continuously over time on the basis of a set of differential equations defining the rates of change of state variables. 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 stochastically 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.