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)
Introduction
- 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
