Many advances in science were achieved from the observation of nature. Thousands of years of natural selection and evolution made certain living beings develop notable abilities and characteristics, that still nowadays surprise us with their complexity and adaptability. Some fields of science make constant use of intensive observation of these characteristics, in order to reproduce them in a controlled environment, with the objective of developping methods and tools based on them. Examples of methods developped this way are ant colony algorithms. Ant Colony Systems are mathematical models based on the behavior of ants when immersed in colonies of likely individuals. Ants are simple individuals, however capable of interacting with each other, obtaining benefits from this practice. These models are very useful in solving large combinatory optimization problems, usually too complex to be solved by exact optimization methods, and represent an important and incipient field of study in production engineering. This work aims to describe some ant colony algorithms, used in combinatory/discrete optimization problems. Particularly, the focus of this work will be in the application of these algorithms to the vehicle routing problem with time windows. A form of implementation of the algorithm in Matlab environment will be proposed and tested in standard problems used as benchmarking in the literature.