## SIMULATION operations. Gordon focused on a block diagram interface

SIMULATION
AND MODELLING – MOTIVATION

Modelling
is a method that is used in creation of a virtual representation of a real
world system inclusive of software and hardware. If the basis of the software
components are driven by mathematical relationships then you can simulate this
virtual representation to see how your model will behave given certain
conditions.

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History
and background of modelling

THE PRE-COMPUTER ERA 1777-1975

The Monte Carlo
method is generally considered to have originated with the Buffon “needle
experiment” in 1777. The experiment is to “throw” needles onto a plane with
equally spaced parallel lines in order to estimate the value of ?.

These results were
published formulating what is now known as Student’s t-distribution. Due
to incomplete analytical results, he used a crude form of manual simulation to
verify his conjecture about the exact form of the probability density function
for Student’s t-distribution.

THE
FORMATIVE PERIOD (1945–1970)

Here the first
general computers were created such as the ENIAC. In 1950 the
availability of general-purpose electronic computers increased setting the
stage for the rapid prolife-ration of simulation techniques and applications in
other disciplines. Keith Douglas Tocher, a professor of operational research at
the University of Southampton, developed the General Simulation Program (GSP),
the first general-purpose simulator, as a tool for systematically building a
simulation of an industrial plant that comprises a set of machines, each
cycling through states such as busy, idle, unavailable, and failed.

Tocher’s preeminent contributions to simulation include the
three-phase method for timing executives, the first text-book in simulation, The
Art of Simulation (1963), and the wheel chart or activity-cycle diagram
(ACD) in 1964. The ACD became a cornerstone of simulation teaching in the UK
and the core of research in program generators during the 1970s. It is
especially noteworthy that Tocher conceived and implemented an approach to
combined simulation (discrete-event and continuous model execution) well before
its appearance in a US simulation language

Geoffrey Gordon during the period 1960–1961, introduced the
General Purpose System Simulator, which was later renamed the General Purpose
Simulation System (GPSS). GPSS was designed to facilitate rapid simulation
modeling of complex teleprocessing systems involving, for example, urban
traffic control, telephone call interception and switching, airline reservation
processing, and steel-mill operations. Gordon focused on a block diagram
interface because he feared that programming might be too great a challenge for
engineers

Between 159 and 1963 central problems of simulation model
construction were established as

Modular design of simulation programs for easy revision;

Management of computer memory;

Control of error arising from the
discretization of all continuous quantities that is inherent in digital
simulation

Design and implementation of an efficient

Management of files containing the
simulation’s entities.

The main problems in using
simulation include the strategic problem of designing a simulation
experiment and the following tactical problems on how to run the
simulations specified in the experimental design:

The start-up
problem, i.e., determining when a simulation is in equilibrium (steady state)
so that any transients caused by the simulation’s initial condition have died
out;

Estimating the
precision (variance) of simulation-based estimators of steady-state
performance; and

Performing precise
comparisons of alternative system simulations or the best of a competing set of
alternative systems or system configurations

All this was solved by Conway
through the use of statistical ranking-and-selection procedures, which are now
widely used in practice and are the basis for much ongoing research

THE EXPANSION PERIOD (1970-1981)

During this period, those in the field of simulation
developed enhanced modeling tools and analytical tools. Some advances with
respect to analytical work include, for instance, contributions to variate
generation; contributions to output analysis; developments in input modeling;
and the study of modern optimization techniques

importance of computer modelling and simulation

Training to enhance motor and operational
skills(and associated decision making skills)

Education

Evaluating alternative courses of action

Acquisition

Operational reports

Engineering design

Prototyping

Diagnosis

Proof of concept

Understanding

computer modelling and simulation can help as a teaching aid to explain
concepts more clearly or even in help to anticipate future crisis e.g economy
crashes.

Applications
of modelling simulation

virtual simulation (i.e., using virtual equipment and real people
(human-in-the-loop) in a simulation study) aircraft simulator for pilot
training

augmented reality simulation (such as in-flight pilot training

virtual body for medicine

nuclear reactor simulator

power plant simulation

Simulation for the teaching/learning of dynamic systems (which may
have trajectory and/or structural behavior): simulation of adaptive systems,
time-varying systems, evolutionary systems.

use of simulation to provide predictive displays (in economy, in
other complex systems)

policy modelling and simulation

drug modelling and simulation

defense acquisition

virtual ship (as a platform to integrate several components)

earthquake simulation to design better structures: buildings,
bridges

chip prototyping

engine prototyping

on-line use of simulation to compare real-system’s behavior and
simulated behavior to detect anomalies in the functioning of an equipment

simulation of safe disposal of nuclear fuel waste (for tens of
thousands of years

scientific simulations to understand reality