Sistemas de Ayuda a la Decisión Multicriterio

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Créditos
4.5
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
URV;CS
Web
moodle URV
Mail
The student will be introduced to the research area of Multicriteria Decision Aid (MCDA).
The course covers three main issues:
(1) Preference structures for representing the interests of the decision maker. Special attention will be paid to the use of non-numerical information, such as linguistic variables, fuzzy sets or ontologies.
(2) Exploitation techniques of the user information to solve the decision problem. The two main approaches to MCDA will be studied: Multiattribute Utility Theory and Outranking Relations. At the end of the course, the student will have to know the theory, properties, advantages and drawbacks of those methods.
(3) Use of MCDA techniques in combination with other fields (f.i. Geographical Information Systems, Recommender Systems).
Free software will be used to practise.

Horas semanales

Teoría
1.8
Problemas
0
Laboratorio
0.9
Aprendizaje dirigido
0
Aprendizaje autónomo
4.5

Objetivos

  1. Recognize the main components of a decision making problem and decide the most appropriate modelization method.
    Competencias relacionadas: CEA12, CG3, CEP3,
  2. Build a preference model according to the heterogeneous data types.
    Competencias relacionadas: CEA12, CT7,
  3. Make an appropriate selection and use of aggregation operators.
    Competencias relacionadas: CEA12, CEP3,
  4. Study and apply methods based on the Multi-Attribute Utility Theory.
    Competencias relacionadas: CEA12, CEP3, CT4, CT7,
  5. Study and apply methods based on Outranking models for MCDA.
    Competencias relacionadas: CEA12, CEP3, CT4, CT7,
  6. Identify the relations between MCDA (Multi-criteria Decision Aiding) and AI (Artificial Intelligence)
    Competencias relacionadas: CEA12, CEP3,

Contenidos

  1. 1 Introduction
    1.1 The decision making problem. Formalization.
    1.2 MCDA applications
  2. 2 Preference representation models
    2.1 Data types
    2.2 Family of criteria
    2.3 Uncertainty
  3. 3 Multi-Attribute Utility Theory
    3.1 Introduction
    3.2 Steps: aggregation and exploitation.
    3.3 Aggregation operators. Properties.
  4. 4 Models based on outranking relations
    4.1 Introduction
    4.2 Outranking relations
    4.3 ELECTRE
  5. 5 MCDA and AI
    Use of MCDA in combination with other intelligent techniques, like intelligent recommender systems.

Actividades

Actividad Acto evaluativo


Exam

Final exam with questions and exercices
Objetivos: 1 2 3 4 5
Semana: 15 (Fuera de horario lectivo)
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Research report with an oral presentation

The student will make a survey on some topic, in group.The report is delivered to the teacher. An oral presentation will be done at class.
Objetivos: 1 4 5 6
Semana: 11
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
2h
Aprendizaje autónomo
20h

Solving practical exercices with software tools

The student will use a free software to solve some exercises. Some of them will be reported in a short document delivered to the teacher.
Objetivos: 1 2 3 4 5 6
Semana: 15
Teoría
0h
Problemas
0h
Laboratorio
1.5h
Aprendizaje dirigido
0h
Aprendizaje autónomo
9.5h

Lectures

The lecturer explains the theoretical conceps of the subject with examples. Some complementary materials will be given to the students.
Objetivos: 1 2 6
Contenidos:
Teoría
27h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
26h

Practical exercises at the computer lab

The student will use a free software to solve some exercises. Some of them will be reported in a short document delivered to the teacher.
Objetivos: 2 3 4 5
Contenidos:
Teoría
0h
Problemas
0h
Laboratorio
12h
Aprendizaje dirigido
0h
Aprendizaje autónomo
12h

Metodología docente

Oral exposition of the teacher
Oral presentations of the students
Practical exercices with software tools
Solving exercices in class

Método de evaluación

Solving practical exercices with software tools 30%
Research report with an oral presentation 30%
Exam with short questions 40%

The student must reach a minimum qualification in the exam in order to pass the course.

Bibliografía

Básica:

  • Multiple criteria decision analysis : state of the art surveys - Figueira, José; Greco, Salvatore; Ehrgott, Matthias, Springer, c2005. ISBN: 978-0-387-23067-2
    http://cataleg.upc.edu/record=b1269711~S1*cat
  • Modeling decisions : information fusion and aggregation operators - Torra i Reventós, Vicenç; Narukawa, Yasuo, Springer, cop, 2007. ISBN: 978-3-540-68789-4
    http://cataleg.upc.edu/record=b1308961~S1*cat
  • Multi-criteria Decision Analysis: Methods and Software - Alessio Ishizaka, Philippe Nemery, Wiley, 2013. ISBN: 978-1-119-97407-9
  • Multicriteria Decision Aid and Artificial Intelligence - Doumpos, Michel, Grigoroudis, Evangelos, Wiley, 2013. ISBN: 978-1-119-97639-4

Web links

Capacidades previas

None