Modelling and Simulation

Code:

M.EIC038

Acronym:

MS

Keywords
Classification	Keyword
OFICIAL	Artificial Intelligence

Instance: 2021/2022 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EIC	26	Syllabus	2	-	6	39	162

Teaching language

English

Objectives

The goals of this course unit are:

Learning outcomes and competences

After the successful completion of this course unit, students should be capable of:

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

There is no mandatory prerequisite. Knowledge of (object-oriented) programming and statistics are desirable.

Program

1. Review and presentation of concepts:




1. Simulation as an engineering methodology;


2. Modelling (realism, abstraction) and types of models (normative, behavioural….);


3. Modelling metaphors and simulation techniques;


4. Data preparation and treatment;


5. Modelling of complex systems and stochastic processes;




2. Simulation projects:




1. Basic techniques of simulation: continuous, discrete and probabilistic simulation;


2. Simulation life cycle: data modelling, collection and preparation; test, calibration and validation of models, results analysis and implementation;


3. Languages and environments of simulation.




3. Advanced topics in modelling and simulation:




1. Object-oriented simulation;


2. Distributed simulation;


3. Visual interactive modelling and simulation;


4. Intelligent simulation;


5. Agent-based modelling and simulation;


6. Simulation environments and simulation of environments;




4. Advanced applications of simulation;


5. Project.

Mandatory literature

Brito António Ernesto da Silva Carvalho; Simulação por computador. ISBN: 972-98726-2-7
Law Averill M.; Simulation modeling and analysis. ISBN: 0-07-059292-6
Chung Christopher A.; Simulation modeling handbook. ISBN: 978-0-203-49646-6
Banks Jerry 070; Discrete-event system simulation. ISBN: 0-13-088702-1

Complementary Bibliography

Various Authors; Papers in Conferences and Scientific Journals, ACM SIGSIM, SCS, IEEE, Eurosis

Teaching methods and learning activities

Teaching methodology will include:

• Theory classes, to present and to discuss conceptual aspects;


• Practical tutorial-based classes, to develop a simulation project;


• Development of a simulation project;


• Writing-up of project report, in paper format;


• Oral presentation of project results.

Software

Mesa: Agent-based modeling in Python
Repast simulation suite
MATSim: large-scale agent-based transport simulations
NetLogo multi-agent programmable modeling environment
SUMO: Simulation of Urban MObility
SimPy: Discrete event simulation for Python

keywords

Physical sciences > Computer science > Modelling tools
Technological sciences > Engineering > Simulation engineering

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	20,00
Trabalho prático ou de projeto	50,00
Apresentação/discussão de um trabalho científico	10,00
Teste	20,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	12,00
Elaboração de projeto	63,00
Estudo autónomo	24,00
Frequência das aulas	39,00
Trabalho de investigação	24,00
Total:	162,00

Eligibility for exams

To be admitted in the assessment process, students have to reach markings >=8.0 in the continuous assessment (CA) components.

Calculation formula of final grade

Evaluation components:

• Project development (P): 50%


• Project report/paper (R): 20%


• Project demo/presentation and discussion (D): 10%


• Individual assessment test (T): 20%

Final mark: F = 0.5*P + 0.2*R + 0.1*D + 0.2*T

Examinations or Special Assignments

• Paper writing-up;


• Project development;


• Oral presentation of project results;


• Individual assessment teset.

Special assessment (TE, DA, ...)

Students with especial satatus should present project, paper and oral demonstration of results, according to the assessment system of the course unit.

Classification improvement

Improvments in classification require improvements in the project and paper components, respectively.

Observations

The kick-off class for the Modelling & Simulation course will be held on October 18, on Zoom, at the following link:

https://videoconf-colibri.zoom.us/j/81461186136?pwd=enVHMXVIaEdpbDQwZ09OMlIrTnBaUT09

Class starts at 9.30 am.