Discrete-event simulation: Difference between revisions

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In addition to next-event time progression, there is also an alternative approach, called '''incremental 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.<ref name="matloff">{{cite web|last=Matloff|first=Norm|title=Introduction to Discrete-Event Simulation and the SimPy Language|url=http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf|access-date=24 January 2013}}</ref> Because not every time slice has to be simulated, a next-event time simulation can typically run faster than a corresponding incremental 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 equation|differential equations]] defining the rates of change offor state variables.

In the past, these three types of simulation have also been referred to, respectively, as: event scheduling simulation, activity scanning simulation, and process interaction simulation. It can also be noted that there are similarities between the implementation of the event queue in event scheduling, and the [[Scheduling (computing)|scheduling queue]] used in operating systems.

== Example ==

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==Components==

In addition to the logic of what happens when system events occur, discrete event simulations include the following:

* Priority queue,

* Animation event handler, and

* Time re-normalization handler (as simulation runs, time variables lose precision. After a while all time variables should be re-normalized by subtracting the last processed event time).

===State===

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===Events list===

{{redirect|Future event list|lists of future events|Timelines of the future}}

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.{{cn|date=March 2022}} The event list is also referred to as the ''future event list'' (FEL) or ''future event set'' (FES).<ref>{{Cite journal |last1=Park |first1=Hyungwook |last2=Fishwick |first2=Paul A. |date=2010|title=A GPU-Based Application Framework Supporting Fast Discrete-Event Simulation |url=http://journals.sagepub.com/doi/10.1177/0037549709340781 |journal=Simulation |language=en |volume=86 |issue=10 |pages=613–628 |doi=10.1177/0037549709340781 |s2cid=9731021 |issn=0037-5497|url-access=subscription }}</ref><ref>{{Cite web |last=Dannenberg |first=Roger |title=An Introduction to Discrete-Event Simulation |url=https://www.cs.cmu.edu/~music/cmsip/readings/intro-discrete-event-sim.html |access-date=2022-03-11 |website=[[Carnegie Mellon School of Computer Science]]}}</ref><ref>{{Cite web |last=Güneş |first=Mesut |title=Chapter 3: General Principles |url=https://www.mi.fu-berlin.de/inf/groups/ag-tech/teaching/2012_SS/L_19540_Modeling_and_Performance_Analysis_with_Simulation/03.pdf |access-date=2022-03-11 |website=[[Freie Universität Berlin]]}}</ref><ref>{{Cite journal |last1=Damerdji |first1=Halim |last2=Glynn |first2=Peter W. |date=1998 |title=Limit Theory for Performance Modeling of Future Event Set Algorithms |url=https://www.jstor.org/stable/2634704 |journal=Management Science |volume=44 |issue=12 |pages=1709–1722 |doi=10.1287/mnsc.44.12.1709 |jstor=2634704 |issn=0025-1909|url-access=subscription }}</ref>

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.{{cn|date=March 2022}}

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===Hospital applications===

An operating theater is generally shared between several surgical disciplines. Through better understanding the nature of these procedures it may be possible to increase the patient throughput.<ref>{{cite journal |author1=John J. Forbus |author2=Daniel Berleant |title=Discrete-Event Simulation in Healthcare Settings: A Review | doi=10.3390/modelling3040027 |volume=3 |journal=Modelling |pages=417–433|year=2022 |issue=4 |doi-access=free |arxiv=2211.00061 }}</ref> Example: If a heart surgery takes on average four hours, changing an operating room schedule from eight available hours to nine will not increase patient throughput. On the other hand, if a hernia procedure takes on average twenty minutes providing an extra hour may also not yield any increased throughput if the capacity and average time spent in the recovery room is not considered.

===Lab test performance improvement ideas===

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* [[Stochastic process]] and a special case, [[Markov process]]

* [[Queueing theory]] and in particular [[birth–death process]]

* [[DEVS|Discrete Event System Specification]]

* [[Transaction-level modeling]] (TLM)

Computational techniques: