- Discrete Events Simulation Modeling Approaches
- Discrete Events Simulation Use Cases
- SimPy for Discrete Events Simulation
The complexity of many real-world systems involves unaffordable analytical models, and consequently, such systems are commonly studied by means of simulation. Different types of simulation apply depending on the nature of the system under consideration. In the last chapter we focused on static simulation, also called Monte Carlo simulation.
In this chapter we discuss discrete-event simulation (DES) which is a specific technique for modelling stochastic, dynamic and discretely evolving systems.
Recall that different types of simulation apply depending on the nature of the system under consideration. A common model taxonomy classifies simulation problems along three main dimensions:
- deterministic vs. stochastic
- static vs. dynamic (depending on whether they require a time component)
- continuous vs. discrete (depending on how the system changes).
For instance, Monte Carlo methods are well-known examples of static stochastic simulation techniques. On the other hand, discrete-event simulation (DES) is a specific technique for modelling stochastic, dynamic and discretely evolving systems.
Customers arriving at a bank, products being manipulated in a supply chain, or packets traversing a network are common examples of such systems.
The discrete nature of a given system arises as soon as its behavior can be described in terms of events, which is the most fundamental concept in DES.
An event is an instantaneous occurrence that may change the state of the system, while, between events, all the state variables remain constant.
#DES Software
Specialized Software: ARENA Software. Python Libraries:
Discrete Events Simulation Modeling Approaches