Critical Thinking

MODEL: of simulation models: continuous, discrete-event, combined discrete-continuous.

MODEL: A representation of either a real
object or system of objects so as to visualize its appearance and analyze its
behavior when compared to the real object.

SIMULATION: Transition from a mathematical
or computer model to a description of the system behavior based of inputs on
the simulation software used. A simulation gives a model meaning as it shows how that
model or object will behave in real life when it has been made.

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 The available types of simulation models: continuous,
discrete-event, combined discrete-continuous. The
available simulation
programming languages: GPSS, SIMSCRIPT, Arena, SIMULA, Simio, SLAM, MoD Auto, Simio.

The history
of simulation and modelling dates back to the long
years of World War II. In the early 1940s
two major developments paved the way for the growth of the field of simulation: 1) The making of
the first general-purpose electronic computers such as the ENIAC, 2) The work of Stanislaw Ulam, John von Neumann in
the use of the Monte
Carlo method on electronic computers in order to solve certain problems involved in
neutron diffusion being used to design a hydrogen bomb. Thus, these two mathematicians introduced the Roulette wheel technique which made it easy for the probabilities of separate events to be merged in a step by step analysis so as to
predict the final outcome of the whole sequence of events.

In 1961 the Gordon Simulator was presented by IBM. At Rand
Corporation Herbert Karr, Harry Markowitz and Bernard Hausner, modified the existing version of SIMSCRIPT in the year 1962 to simulate their inventory
problems. In England J. The CLS was developed by Buxton and J. Laski which was a Control
and Simulation Language. Keith Douglas
Tocher whom was a professor of operational research at the
University of Southampton brought about the General Simulation Program
(GSP), which was the first general-purpose simulator, as a tool which was to be used as a systematically building 
simulation of an industrial plant that comprises a set of machines.

Geoffrey
Gordon joined the Advanced Systems Development Division of IBM in 1960 as
Manager of Simulation Development; and between 1960–1961, he introduced the General-Purpose
Simulation System (GPSS). Which its soul purpose was to be used
for simulation modelling
of complex teleprocessing systems such as urban traffic control, telephone
call interception, airline reservation processing.

Nygaard and
Don Knuth and produced (SOL)a symbolic Language for General
Purpose System Simulation. In the late 80s 
SIMANIV and CINEMAIV were developed and these
where the newest in
simulation and animation software by systems modelling. In 1984 the first simulation
language specifically designed for modelling manufacturing systems was
developed.

In the expansion period Pritkser and Hurst’s developed the GASP IV;
Kiviat, Villanueva, and Markowitz’s development of SIMSCRIPT II.5; Pritsker and
Pegden’s develop-ment of SLAM; Pegden’s development of SIMAN; Nance’s conical
method for object-oriented model development; Schruben’s event graphs; the
development of specialty simulation products for niche markets; and Sargent’s major
contributions to formal verification and validation of
modelling and simulation.

IMPORTANCE OF MODELLING AND SIMULATION

Modelling and simulation can facilitate
understanding a system’s behavior without actually testing the system in the
real world. simulation can aide in analysing experiments that occurs totally in
the software used , or in human-in-the-loop environments where simulation
represents systems or generates data needed to meet experiment objectives. simulation
can also be used to train trainees by using a modelled virtual environment
which represents the real one which would otherwise be difficult or expensive
to produce.

Using simulations most if not always cheaper,
safer and many times more ethical than conducting real-world experiments. An
example is supercomputers being used to model and simulate the detonation of
nuclear devices together with their effects for future preparedness in the
event of a nuclear explosion. Simulations is often  more realistic than traditional experiments,
as they allow the free configuration of environment parameters to how the user
wants them to be found in the operational application field of the final
product.

In Simulation and modelling coherent
synthetic environment can be modelled and this allows for integration of
simulated systems in the early analysis phase via mixed virtual systems with
first prototypical components to a virtual test environment for the final system.
Simulations can often be conducted faster than real time. This allows using
them for efficient if-then-else analyses of different alternatives, in
particular when the necessary data to initialize the simulation can easily be
obtained from operational data. Also, simulation adds further decision support
to simulation systems to the tool box of traditional decision support systems.

APPLICATION OF MODELLING AND SIMULATION

Currently Simulation is the most growing
topics that engineers’s face in the workplace. It can also be implemented on a corporation level, regardless of the industry. Quality, safety
and productivity are all affected by Simulation. Simulation is one of the tools that are used to increase the production capacity of various
different companies and industries.

The apparent success of many military
simulations for the purposes of training, doctrine development, investigation
of advanced system concepts, mission rehearsal, and assessments of threats and
countermeasures has resulted in their increased use for these purposes. Also,
still in the defense department modelling and simulation is greatly used in
training users of new systems simulation can also be used as a supporting
structure to aid in arguments presented in the analysis of alternatives to
justify going ahead with system development.

Modeling and Simulation can enhance the
results of clinical researchers by providing or aiding in a  better-suited, more precise hypotheses to be
tested in future experiments. In fact, preliminary studies by researchers at the
University of Pittsburgh suggest that “in silico” modeling could have prevented
universally disappointing results of ant cytokine trials in patients with
sepsis by pointing out the critical importance of timing of experimental
intervention in relation to infectious insult.M can provide an invaluable
platform for 1) designing clinical experiments, 2) process improvement of care
delivery, 3) forecasting and decision support at the patient’s bedside, and 4)
informing healthcare policy decisions.

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