You could run this simulation to any point in time and precisely know the history of the vehicle’s latitude, longitude, and altitude through all stages of flight.īut perhaps we aren’t concerned with just one aircraft. One approach would be a trajectory-based simulation in which the model diligently tracks the position of the aircraft. Let’s take a closer look.Ĭonsider the task of simulating the flight of an aircraft. It’s these kinds of applications, when questions such as resource management are at play, that low-level details become irrelevant and discrete-event simulations become useful. If you want to predict bit drop rates in a data network, you likely don’t care about the voltage across every single transistor.
#DISCRETE EVENT SIMULATION CODE#
For example, if your task is to understand how the number of cashiers impacts line lengths at a grocery store, you probably wouldn’t worry about simulating every bar code swipe. In many cases, discrete-event simulation is a straightforward way to model the problem and acquire the desired data.
Box wrote that "all models are wrong, but some are useful." Understanding the purpose of your simulation dictates how you approximate the system. Discrete-event simulations are often used to answer questions concerning scheduling, resource allocation, and capacity planning. Ultimately though, adoption will depend on what problem you’re attempting to solve. In situations where the choice is less clear, you may adopt a discrete-event approach due to the computational advantages it offers over a continuous dynamics simulation. Some processes lend themselves well to discrete-event simulation due to their event-driven nature. Let’s examine why they are so powerful for certain applications, and why you might use them over other simulation techniques.
Discrete-event simulation analyzes the behavior of a dynamic system by approximating it as a sequence of instantaneous occurrences.
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