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