Discrete Event Simulation in Python

Overview

Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Python's SimPy package.

Have you ever been asked to optimize your industry or business processes and resources? This course will teach you how to tackle this optimization problem through discrete-event simulations. By leveraging Python’s SimPy package, you’ll develop digital twins for different industrial processes and resources based on discrete-event simulations. You’ll encounter several real-world examples and gain the confidence to create operational discrete-event models from scratch that can be used as “virtual test beds” to study management options, test scenarios, and study optimization strategies.

Syllabus

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.

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.

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.

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.