Course
Discrete Event Simulation in Python
AdvancedSkill Level
Updated 04/2026PythonProbability & Statistics4 hr16 videos55 Exercises4,650 XP2,595Statement of Accomplishment
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Discover Discrete-Event Simulation
Have you ever been asked to optimize your industry or business operations? In this course on discrete-event simulation in Python, you will learn how to tackle the optimization of a myriad of processes running in parallel or in sequence.Explore Process Optimization
Manufacturing, transportation, logistics, and supply-chain activities may require the management of several processes running in parallel or in sequence. Optimizing these processes can be a daunting task, even for small companies, but it is an essential journey needed to increase profitability, tackle bottlenecks, and improve the management of resources.Develop Digital Twins for Real-World Processes
By leveraging Python’s SimPy package, you’ll develop digital twins for different types of industrial processes based on discrete-event simulations. You’ll encounter several real-world examples, from car production lines and eCommerce to road traffic management and supply-chain activities. After completing this course, you will have the confidence to develop operational discrete-event models that can be used as “virtual living labs” for incrementally testing the effectiveness and cost-benefit of different management and optimization strategies.Prerequisites
Introduction to Statistics in PythonPython Toolbox1
Introduction to Dynamic Systems and Discrete-Event Simulation Models
Let’s unravel the power of discrete-event simulations. To begin this course, you’ll learn to identify problems where discrete-event simulations can be helpful in supporting management and decision-making. You’ll also learn the main components of discrete-event models and how to interpret model outputs. Finally, you’ll build your first “queue” discrete-event model.
2
Developing Discrete-Event Models Using SimPy
Discover the power of the SimPy package to streamline your discrete-event simulations. In chapter 2, you’ll learn how to build a SimPy model environment and how to add processes and resources. You’ll also learn the different types of resources available, as well as options to control and schedule events. To finish this chapter, you’ll build a complete SimPy model for an aircraft assembly line.
3
Mixing Determinism and Non-Determinism in Models
Explore the types of processes that you can add to discrete-event models. You’ll learn to distinguish between deterministic and non-deterministic processes and how to represent them in models. You’ll also learn how to randomize events (or processes), which is critical to simulate non-deterministic events. Finally, you’ll build a SimPy model combining both deterministic and non-deterministic processes.
4
Model Application, Clustering, Optimization, and Modularity
You’ll learn optimization methods to maximize the impact of your discrete-event models. You’ll learn how to perform simulation ensembles using Monte Carlo approaches and discover how to identify clusters in your model results to help you understand its behavior and identify critical processes and tipping points. You’ll also use objective functions to set targets for your model optimization efforts. To end this course, you’ll explore how to make your model scalable so that it can grow stable and in a controlled manner.
Discrete Event Simulation in Python
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FAQs
What Python package is used for discrete event simulation in this course?
You use the SimPy package to build simulation environments, add processes and resources, and model both deterministic and non-deterministic events in Python.
What kind of real-world problems can I solve after completing this course?
You can build digital twins of industrial processes, optimize resource allocation, simulate queue systems, and test management scenarios using virtual test beds.
Is this course for beginners or experienced programmers?
It is an advanced course requiring solid Python skills, including functions, pandas, and statistics. You should be comfortable writing custom functions and working with data.
Does the course cover Monte Carlo simulation methods?
Yes. Chapter 4 teaches simulation ensembles using Monte Carlo approaches, along with clustering model results and using objective functions for optimization.
What specific simulation model do I build during the course?
Among other models, you build a complete SimPy model for an aircraft assembly line, combining deterministic and non-deterministic processes with various resource types.
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