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