Simulation is the replication of a given system by using
artificial methods. It can be purely analytical (through mathematical equations)
or computational. Although it is possible to replicate low and medium complexity
activities analytically, it is extremely hard to build flexible models and get
accurate results, therefore the use of computers becomes essential.

Computer simulations can be used to better understand the
impacts of specific decisions, policies, or systems configurations through the
use of computer simulation of real systems. Computer simulations can also be
used in educational settings in order to develop specific skills, in which
students control part of the computer simulation variables through user
interfaces.

There are four main simulation technics:

1.
Monte Carlo simulation

2.
Discrete-Event
Simulation

3.
System Dynamics

4.
Agent-Based Simulation

Monte Carlo Simulation uses, repeatedly, random sets of
numbers from known probability distribution of different sources of uncertainty
in order to compute the results of a mathematical model or algorithm (i.e., the
system's model), from which we can infer the general behavior or performance of
that system. It is used in practice when the behavior of the system cannot be
easily calculated analytically.

Discrete-Event Simulation (DES) aims to create simulation
models of queuing-type systems, in which time moves forward either by equal time
increments or from one event to the next. Events and flows between system
components occur according to known probability distributions specifying
processing and transit times and priority rules.

System Dynamics (SD) aims to model complex systems in
order to analyze their general behavior. This technique uses a top-down modeling
approach based on stocks, flows, feedback loops and time delays, in order to
simulate the complex interactions between the components of a system. In other
words, System Dynamics aims to capture the ripple effect of changes to these
components throughout the entire system, in order to model and study the
resulting non-linear behavior of the system. System Dynamics only models the
mutual dependencies between these components. It does not model the elementary
interactions between the individual elements of the system

Agent-Based Simulation (ABS) is an emerging simulation
tool (Macal & North, 2006), which takes a bottom-up approach to model the
individual behaviors and interactions of a system's elements, referred to as
agents. Therefore, instead of modeling the relationships between the components
a system, Agent-Based Simulation captures how the individual elements of a
system behave with respect to their own local environment and state, and how
they interact, communicate, make collective decisions, or influence each other.
The Agent-Based Simulation modeling paradigm uses theoretical models to capture
individual behaviors.

Simulation applied to different sectors:

1.
Manufacturing

2.
Services

3.
Logistics

4.
Healthcare

5.
Energy

6.
Economics and Finances

7.
Environment and
Ecosystems

8.
Social Dynamics

9.
Others