Simulazione
A.Y. 2021/2022
Learning objectives
To build an overall view of both theoretical and practical topics in
descriptive mathematical modeling, stochastic simulation paradigms, and
analysis of numerical results produced by simulation tools.
descriptive mathematical modeling, stochastic simulation paradigms, and
analysis of numerical results produced by simulation tools.
Expected learning outcomes
Knowledges:
- mathematical methods for the descriptive modeling of both
deterministic and stochastic systems
- simulation paradigms (discrete events, agent based, system dynamics)
- statistical techniques for building, validating and improving
simulation schemes
- main internal implementation techniques of simulation software tools
Competences:
- to analyze and provide descriptive modeling of complex systems
- to choose suitable simulation paradigms
- to validate and critically analyzing numerical results
Skills:
- to build and formalize a descriptive mathematical model
- to use professional software tools to implement simulation models
- to use professional software tools to analyze the results produced by
simulation tools, and to extract knowledge from them
- to write scientific texts for presenting models, experiments and
results
- mathematical methods for the descriptive modeling of both
deterministic and stochastic systems
- simulation paradigms (discrete events, agent based, system dynamics)
- statistical techniques for building, validating and improving
simulation schemes
- main internal implementation techniques of simulation software tools
Competences:
- to analyze and provide descriptive modeling of complex systems
- to choose suitable simulation paradigms
- to validate and critically analyzing numerical results
Skills:
- to build and formalize a descriptive mathematical model
- to use professional software tools to implement simulation models
- to use professional software tools to analyze the results produced by
simulation tools, and to extract knowledge from them
- to write scientific texts for presenting models, experiments and
results
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Theory lecture topics:
Lecture 1. Introduction to Simulation paradigms.
Part 1. Modeling with Random Variables.
Part 2. Generating Random Numbers and Random Variables
Part 3. Statistical Analysis of Simulation Results.
Modeling and software tool tutorials:
Tutorial 1. Agent Based simulation (Diffusion of Technologies)
Tutorial 2. Discrete Events Based simulation (Supply Chain, Job Shops).
Tutorial 3. System Dynamics Simulation (Diffusion of influence)
Lecture 1. Introduction to Simulation paradigms.
Part 1. Modeling with Random Variables.
Part 2. Generating Random Numbers and Random Variables
Part 3. Statistical Analysis of Simulation Results.
Modeling and software tool tutorials:
Tutorial 1. Agent Based simulation (Diffusion of Technologies)
Tutorial 2. Discrete Events Based simulation (Supply Chain, Job Shops).
Tutorial 3. System Dynamics Simulation (Diffusion of influence)
Prerequisites for admission
Probability Calculus and Statistics. Basic computer programming skills.
Teaching methods
Classroom lectures, software tutorials.
Teaching Resources
S. Ross "Simulation". 5th edition (2014).
Assessment methods and Criteria
Practical project and oral theory exam.
Professor(s)