Simulation

Credits
6
Types
Specialization complementary (Software Engineering)
Requirements
Department
EIO
Simulation is a discipline conceived to represent, to model and to understand the behavior of complex systems and to predict their future behavior. This course provides students with the necessary knowledge and tools to be able of building complex simulation models, the use of standard simulation languages, the analysis of input data, the design of experiments and the analysis of results. Visualization, use cases and issues related to performance are also discussed in the course.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0.24
Autonomous learning
5.76

Competences

Technical Competences

Common technical competencies

  • CT2 - To use properly theories, procedures and tools in the professional development of the informatics engineering in all its fields (specification, design, implementation, deployment and products evaluation) demonstrating the comprehension of the adopted compromises in the design decisions.
    • CT2.1 - To demonstrate knowledge and capacity to apply the principles, methodologies and life cycles of software engineering.
    • CT2.4 - To demonstrate knowledge and capacity to apply the needed tools for storage, processing and access to the information system, even if they are web-based systems.

    • Technical Competences of each Specialization

    Software engineering specialization

  • CES1 - To develop, maintain and evaluate software services and systems which satisfy all user requirements, which behave reliably and efficiently, with a reasonable development and maintenance and which satisfy the rules for quality applying the theories, principles, methods and practices of Software Engineering.
    • CES1.1 - To develop, maintain and evaluate complex and/or critical software systems and services.
  • CES2 - To value the client needs and specify the software requirements to satisfy these needs, reconciling conflictive objectives through searching acceptable compromises, taking into account the limitations related to the cost, time, already developed systems and organizations.
    • CES2.2 - To design adequate solutions in one or more application domains, using software engineering methods which integrate ethical, social, legal and economical aspects.

    • Transversal Competences

    Reasoning

  • G9 [Avaluable] - Capacity of critical, logical and mathematical reasoning. Capacity to solve problems in her study area. Abstraction capacity: capacity to create and use models that reflect real situations. Capacity to design and perform simple experiments and analyse and interpret its results. Analysis, synthesis and evaluation capacity.
    • G9.3 - Critical capacity, evaluation capacity.

    • Sustainability and social commitment

  • G2 [Avaluable] - To know and understand the complexity of the economic and social phenomena typical of the welfare society. To be capable of analyse and evaluate the social and environmental impact.
    • G2.3 - To take into account the social, economical and environmental dimensions, and the privacy right when applying solutions and carry out project which will be coherent with the human development and sustainability.

    • Third language

  • G3 [Avaluable] - To know the English language in a correct oral and written level, and accordingly to the needs of the graduates in Informatics Engineering. Capacity to work in a multidisciplinary group and in a multi-language environment and to communicate, orally and in a written way, knowledge, procedures, results and ideas related to the technical informatics engineer profession.
    • G3.1 - To understand and use effectively handbooks, products specifications and other technical information written in English.

    • Objectives

    1. Being able to write a technical article and correctly express concepts in English language.
      Related competences: G9.3, G3.1,
    2. Ability to produce a consulting project.
      Related competences: G9.3, CT2.1, CES1.1, CES2.2, G2.3, CT2.4,
      Subcompetences
      • Being able to assess the impact of the proposed solutions in the context of Sustainable Development Goals (SDG)
    3. Ability to develop a discrete event simulation system study.
      Related competences: CT2.1, CES1.1,

    Contents

    1. Introduction
      What is a simulation study? A practical approach by presenting a real project that will allow students to identify the phases that must be followed for the development of a valid and useful simulation study.
    2. Simulation and Statistical methods
      Randomness as the cornerstone of modeling and experimentation. Statistical distributions, generation of numbers and random variables.
      Some known distributions and their application in simulation models. Monte Carlo Methods and simulation sampling process.
    3. Simulation paradigms.
      Introduction to the main paradigms in simulation and applicability of them. Introducing Netlogo, a specific IDE based on agents-based models. ABM system development.
    4. System modeling and related data.
      How to build a simulation model using specification languages like UML, SDL ...
      Input data analysis. How to fit empirical data to random distributions.
    5. Discrete Event Simulation (DES)
      How a discrete event simulator works, what components are necessary for its development. Integration with third-party applications.
    6. Verification and validation of simulation models.
      Methodologies to buid verified, validated and credible simulation models.
    7. Experimental design and output analysis.
      Basic concepts and methods, the design of experiments in simulation: Scenarios and experiments. Results quality.
    8. Presentation and defense of a simulation study
      Multidisciplinary and team work. Presentation and defense of a simulation study for a client. Goals definition and results presentation quality, discussion and future work.

    Activities

    Activity Evaluation act


    Fonaments bàsics de la simulació

    Introducció a l'assignatura, exemples de sistemes i de models. Revisió històrica. En aquesta activitat l'estudiant aprendrà les diferents fases associades a un estudi de simulació i l'existència de simuladors específics i genèrics. Motivar a l'alumne i explicar la importància de la disciplina a través d'exemples reals.
    Objectives: 2 3
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Aleatorietat i Simulació

    En aquesta activitat l'estudiant identificarà l'estreta relació entre l'estadística i els seus mètodes i realitzar un estudi de simulació de qualitat.
    • Laboratory: GPSS language work from two points of view, the first in which we see the system as a user, working one of two paradigms of discrete simulation. Later we will "open" the system to understand its inner workings and learn the main fetarues of a second paradigm.
    Objectives: 2 3
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Simulació basada en agents

    Paradigmes de Simulació. L'estudiant aprendrà a utilitzar un IDE específic orientat a modelització basada en agents (ABM), un enfoc a la simulació social, i comprendrà la diferència entre simuladors event-schedulling i time-step
    Objectives: 2 3
    Contents:
    Theory
    6h
    Problems
    0h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    12h

    Estudi de Simulació

    L'estudiant aprendrà la importància d'establir clarament els objectius i els elements significatius a ser observats, modelats i validats, en l'estudi proposat.
    Objectives: 1 2 3
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Discrete Event Simulation (DES)

    Activitat principal del curs que permetrà a l'estudiant assolir els coneixements teòrics que l'ajudin a desenvolupar un simulador específic orientat a esdeveniments discrets.
    Objectives: 1 2 3
    Contents:
    Theory
    6h
    Problems
    0h
    Laboratory
    6h
    Guided learning
    0h
    Autonomous learning
    24h

    Verificació i Validació de models de simulació

    Descriure les tècniques més usuals per poder Verificar i Validar (VV&A) els models de simulació. Es posa èmfasi en la necessitat d'utilitzar aquestes tècniques per tal de poder emprar el simulador amb garanties de qualitat.
    Objectives: 1 2 3
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    10h

    Disseny d'experiments i Anàlisi de Resultats

    L'estudiant realitzarà el disseny d'experiments que millor s'ajusti el seu estudi per, a posteriori, analitzar els resultats. Prèviament, adaptarà el seu motor de simulació específic per tal que suporti l'execució d'experiments.
    Objectives: 1 2 3
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Writing the final report

    Compiling all the information generated during the course and creating the final report.
    Objectives: 2 3
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    3.6h
    Autonomous learning
    14.4h

    Teaching methodology

    The course is designed taking into account cooperative learning and problem/project-based learning methodologies, complemented with some theoretical sessions intended to develop the set of deliverables with the best guarantees and achievement.

    Evaluation methodology

    The subject follows a mixed assessment method, with reviews of the work developed in the laboratories and a final theoretical exam. Continuous student involvement in all activities is required in order to pass the course.

    Final grade: 0.6*Simulation study 0.4 Exam

    Bibliography

    Basic

    Complementary

    Web links

    Previous capacities

    Statistics.