and 3D virtualization


The large amount of information available through the technologies implemented in Industry 4.0 (IoT, Big Data, cyberphysical systems….), allows simulations to predict the consumption of resources and optimize their use.

A first step to better understand the functioning of a real life system is to make a mathematical and statistical model for the system, which summarizes the essential parts of the system in mathematical language, involving variables, parameters, formulas, probability distributions, relationships, diagrams, etc. A model is a simplified representation of a system that allows predicting its behavior without resorting to experimentation on that system. The use of mathematical models as instruments for evaluating alternatives is increasingly important as most processes are subject to continuous change. In order to make the right decisions, it is necessary to know how the system will respond to a certain action. Simulation is the process of experimenting with a model.

To be useful, a model must incorporate elements of realism and simplicity. On the one hand, the model should serve as a close approximation to the real system and incorporate most aspects of the real system. On the other hand, the model should not be too complex to prevent its understanding and manipulation. When the model is relatively simple, it may be possible to study it analytically through expressions that describe the behavior of certain aspects of the system. For more complex systems, analytical approaches are often much more difficult or impossible to perform. Instead, the system is often analyzed numerically by computer simulation, with the assumption that the simulated system is sufficiently similar to the real system to draw valid conclusions about the latter.


The simulation models are intended to mimic the performance of real life systems. A system is formed by a collection of entities or objects that interact forming a complex whole (Kroese, Taimre, & Botev, 2011). The models can be classified according to the way in which the variables evolve in time:

Continuous Time: Variables evolve continuously over time. They are usually represented by differential equations.

Discrete Time: Variables can only change at certain moments of time.

Discrete Events: Variables can change at any time, but there can only be finite numbers of changes in finite time intervals. The modeling of discrete events (discrete-event simulation -DES-) is the process that represents the performance  of a complex system as a series of well-defined and ordered events and works well in practically any process in which there is a variability. The simulation of discrete events models the operation of a system as a sequence of discrete events over time. Each event occurs at a particular moment in time and marks the change of state in the system. Between consecutive events it is assumed that no changes occur in the system, so the simulation can jump directly from one event to another. Therefore, it becomes an analysis tool for making decisions related to production planning and inventories, with the design of production systems and their supply chains. In the simulation of discrete events events are generated and managed in time through an ordered event queue. In this way, the simulator reads from the queue and triggers new events. This type of simulation is used in the design of most links in the supply chain such as: production lines, processing plants, hospitals, etc.

The discrete event simulation model allows solving processes and systems which analysis by mathematical methods is complex. Logical-mathematical models are constructed that allow imitating or simulating the performance of the real world. The repetition of the simulation a sufficient number of times allows to obtain an artificial history of observations on the behavior of the system or process from which, by means of statistical analysis techniques, it is possible to draw conclusions about the operation of the system. The main characteristic of a system of discrete events is that the system is determined by a sequence of events that occur at random moments of time and the change of state of the system takes place at those moments (Rosete, 2017). This contrasts with the continuous simulation in which the simulation represents the dynamics of the system over time. Because discrete event simulations do not have to simulate each time segment, they can usually run much faster than continuous simulation.

The domain of the application for the simulation. Simulation is a tool that is used to a large extent in the design phase of a system, as it is used in industry. One way in which the use of simulation could be expanded is with the search for new application domains in the sectors in which it is already being used. There is potential for the simulation to be applied in areas such as:

  •  Emulation to help the design of control systems.
  •  Programming.
  •  Prediction of future results.
  •  Real-time control
  •  Training: ideal both for new incorporations and to promote the versatility of positions.

The modeling of human behavior and the interaction with a system of operations. The purpose should be to better understand how human interaction with a system of operations affects the performance of that system and look for ways to improve the actions, behaviors and decisions of human actors. The requirement here is to be able to quickly build and use simulations (very approximate), possibly in a group decision making environment.

The main solutions that NORLEAN offers through NOA are the optimization of decision-making time, the identification of bottlenecks and the increase in the reliability of processes.

As a result, it provides solutions to businesses, offering a high degree of personalization.

In addition to the previous NOA applications, it adds value in the training field, in the logistics of the product and, mainly, the simulation of plant operations in any industry.


Discrete event systems model the performance of a wide variety of systems in engineering and operational research. The applications can be found, for example, in the programming of production, traffic and transport, inventory control, manufacturing, defense, finance, telecommunications and computer systems.