Discrete event simulation with Python (TDADESP)

In this course, students are introduced to the process of codifying the behavior of a complex system from scratch through discrete-event simulation (DES). Both methodological, mathematical and computational aspects of DES will be covered, using Python language as main tool.

Audience (and prerequisites)

Anyone with a basic knowledge in statistics and programming who desires learning how to design and develop a simulation model.

Approaches (Objective)


  • Statistics prerequisites
  • Types of models and motivations
  • Simulation basics
  • Simulation structure

Discrete event modelling

  • Discrete Event Markov Chain
  • Discrete Event Simulation

Simulation inputs

  • Trace-driven VS model-driven
  • Identifying distributions
  • Parameters estimation
  • Goodness-of-fit

Simulation outputs

  • Transient and steady-state
  • Defining metrics
  • Confidence intervals
  • Simulation campaigns