While such an event is unlikely, authorities have to be prepared for the unthinkable, especially with the growing threat of conflict between North Korea and the U. These scenarios once resembled table-top role-playing games where officials predicted the actions of citizens using statistics. Then, with the advent of computers, these simulations became more complex. The latest model digitally recreates the city complete with buildings, roads, power lines, hospitals cell towers and, most importantly, a virtual population of aroundcitizens.
Example[ edit ] A common exercise in learning how to build discrete-event simulations is to model a queuesuch 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. State[ Working capital simulation ] A system state is a set of variables that captures the salient properties of the system to be studied.
The state trajectory over time S t can be mathematically represented by a step function whose value can change whenever an event occurs. Clock[ edit ] The simulation Working capital simulation keep track of the current simulation time, in whatever measurement units are suitable for the system being modeled.
In discrete-event simulations, as opposed to continuous simulations, time 'hops' because events are instantaneous — the clock skips to the next event start time as the simulation proceeds. Events list[ edit ] The simulation maintains at least one list of simulation events. This is sometimes called the pending event set because it lists events that are pending as a result of previously simulated event but have yet to be simulated themselves.
An event is described by the time at which it occurs and a type, indicating the code that will be used to simulate that event.
It is common for the event code to be parametrized, in which case, the event description also contains parameters to the event code. When events are instantaneous, activities that extend over time are modeled as sequences of events. Some simulation frameworks allow the time of an event to be specified as an interval, giving the start time and the end time of each event.
Single-threaded simulation engines based on instantaneous events have just one current event. In contrast, multi-threaded simulation engines and simulation engines supporting an interval-based event model may have multiple current events.
In both cases, there are significant problems with synchronization between current events. The pending event set is typically organized as a priority queuesorted by event time.
Various priority queue implementations have been studied in the context of discrete event simulation  ; alternatives studied have included splay treesskip listscalendar queues and ladder queues.
Random-number generators[ edit ] The simulation needs to generate random variables of various kinds, depending on the system model. This is accomplished by one or more Pseudorandom number generators. The use of pseudo-random numbers as opposed to true random numbers is a benefit should a simulation need a rerun with exactly the same behavior.
One of the problems with the random number distributions used in discrete-event simulation is that the steady-state distributions of event times may not be known in advance. As a result, the initial set of events placed into the pending event set will not have arrival times representative of the steady-state distribution.
This problem is typically solved by bootstrapping the simulation model. Only a limited effort is made to assign realistic times to the initial set of pending events.
These events, however, schedule additional events, and with time, the distribution of event times approaches its steady state.
This is called bootstrapping the simulation model. In gathering statistics from the running model, it is important to either disregard events that occur before the steady state is reached or to run the simulation for long enough that the bootstrapping behavior is overwhelmed by steady-state behavior.
This use of the term bootstrapping can be contrasted with its use in both statistics and computing. Statistics[ edit ] The simulation typically keeps track of the system's statisticswhich quantify the aspects of interest.
In the bank example, it is of interest to track the mean waiting times. In a simulation model, performance metrics are not analytically derived from probability distributionsbut rather as averages over replicationsthat is different runs of the model.Sunflower Nutraceutical (SNC) is a distributor in the Miami, Florida area is a privately owned company.
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