Introduction to discrete-event simulations
1. Introduction to discrete-event simulations
In this video, we will learn about discrete-event models as a type of mathematical model particularly suited for human-driven or human-initiated processes, such as those found in manufacturing, supply-demand activities, and traffic management. Let's dive in.2. Discrete-event models vs. continuous models
We first need to distinguish between discrete-event and continuous models. These treat time in a different manner. While continuous models run simulations at fixed time steps, discrete-event models run at variable time steps. This is because discrete-event models focus on scheduling and running discrete events, which can have different time durations. This means that time is a dummy variable in continuous models, not changing inside the run-loop. However, in discrete-event models, time is a state-variable that needs to be updated inside the model. This fundamental difference between continuous and discrete-event models leads to continuous models typically using for-loops. In contrast, discrete-event models generally use while-loops where time gets updated inside the loop. Let's solidify these concepts by looking at the main model components of discrete-event models and how they can be materialized into code.3. Components of discrete-event models
Typically, discrete-event models have four main model components: state variables, the clock, the event list, and the ending conditions. We will learn what each of these model components is using a code example for a discrete-event model of a taxi company.4. Model components
The discrete-event model is shown on the left. The first component that appears in this model is the ending condition - while time is less than 10. This means that the model will terminate when this condition is met. We need the ending conditions to terminate the simulation because otherwise, the simulation would run forever.5. Model components
Then we have the "clock". The discrete-event model must keep track of the simulation time because the "clock" skips to the next event start time as the simulation proceeds. In other words, in discrete-event simulations, time 'hops' because events are instantaneous.6. Model components
Finally, we have the state variable. In our taxi company model, the state variable is "time" because we want to reduce the time between a customer calling a taxi and being dropped off at the desired location. So, essentially, state variables are variables that characterize the output and performance of the system to be studied.7. Let's practice!
Let's puts these new concepts into practice.Create Your Free Account
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