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Discrete Event Simulation in Python

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

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4 Horas16 Videos55 Ejercicios

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Descripción del curso

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.
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  1. 1

    Introduction to Dynamic Systems and Discrete-Event Simulation Models

    Gratuito

    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.

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    Dynamic systems and discrete-event models
    50 xp
    Dynamic systems
    100 xp
    Decomposing a process into a sequence of events
    100 xp
    Lift: discrete-event model
    100 xp
    Mathematical models of dynamic systems
    50 xp
    Discrete-event model: identify critical processes
    100 xp
    Mathematical models: incorporating key processes
    100 xp
    Introduction to discrete-event simulations
    50 xp
    Developing a discrete-event model
    100 xp
    Running the discrete-event model
    100 xp
  2. 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.

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Colaboradores

Collaborator's avatar
George Boorman
Collaborator's avatar
Maham Khan
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James Chapman

Requisitos Previos

Introduction to Statistics in PythonPython Data Science Toolbox (Part 2)
Diogo Costa (PhD, MSc) HeadshotDiogo Costa (PhD, MSc)

Adjunct Professor, University of Saskatchewan, Canada & CEO of ImpactBLUE-Scientific

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