Dynamic systems and discrete-event models
1. Dynamic systems and discrete-event models
Welcome to this course on discrete-event simulations in Python. In this course, we will learn how to simulate situations which require the management of multiple processes running in parallel or sequence. Discrete-event simulations can help optimize industry or business operations for manufacturing, transportation, logistics, supply chain, and more. Let's dive in.2. What is a dynamic system?
The first concept we will learn about is that of dynamic systems. Dynamic systems are systems that change over time. This is in contrast to steady-state systems, where variables remain unchanged over time. Variables that change in dynamic systems are called state variables. In steady-state systems, on the other hand, even though variables remain unchanged over time, that doesn't mean they are at rest. Let's see how graphs of such systems could look like.3. What is a dynamic system?
As we can see, variables change over time in dynamic systems in purple, but they remain constant in the steady-state systems in pink. Systems can be natural or human-driven, and they can always be classified as either dynamic or steady-state. Let's look at some examples.4. Examples of dynamic systems
There are many examples of dynamic systems in the natural world, such as the weather and ocean systems. But there are also many examples of human-initiated or human-driven processes that are very dynamic, such as road traffic and manufacturing, which can involve multiple processes running in parallel or sequence. Let's now look at some examples of non-dynamic or steady-state systems.5. Examples of non-dynamic systems
There are many examples of steady-state systems in the natural world. For example, a rock at rest does not change its positioning over time unless someone knocks it. So, it is considered a steady-state system. A river flowing calmly on a sunny day is not at rest, but its flow does not change over time, so it's also considered a non-dynamic system. Many human-initiated or human-driven processes also remain unchanged over long periods of time, so they can be considered non-dynamic. For example, satellites rotate around the globe at constant speeds and usually do not change direction, so they are non-dynamic. A boat cruising at constant speeds over large distances is also a steady-state process. Now that we understand what distinguishes dynamic systems from steady-state systems, let's see how discrete-event simulations can be used to model dynamic systems.6. What are discrete-event simulations?
Discrete-event models are mathematical representations of systems, which can be steady-state or dynamic. However, they are particularly useful in addressing the complexity of dynamic systems. They are specifically suited for human-driven activities, particularly when they involve a sequence of interdependent processes that can be decomposed into a series of queueing events. For example, in supply-chain operations, there is first the order, then packaging, then the long-distance transportation, and then the local delivery. Discrete-event models can become a digital twin of a business or industry in the sense that they can virtually mimic real-world processes and be used to test different management strategies before implementation. They can also be used to optimize processes, increase productivity, identify-eliminate bottlenecks, and streamline resource allocation. Let's look into some examples.7. Examples of applications
Construction, manufacturing, supply-chain and logistics, and transportation are all examples of activities that can be broken down into a sequence of interdependent events and simulated using discrete-event models.8. Let's practice!
Now that we understand what dynamic systems and discrete-event simulations are, let's work through some exercises.Create Your Free Account
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