Multi-Criteria Decision Support Systems

Credits
4.5
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
URV;CS
Web
moodle URV
Mail
aida.valls@urv.cat
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.

Weekly hours

Theory
1.8
Problems
0
Laboratory
0.9
Guided learning
0
Autonomous learning
4.5

Objectives

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

Contents

  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.

Activities

Activity Evaluation act


Exam

Final exam with questions and exercices
Objectives: 1 2 3 4 5
Week: 15 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
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.
Objectives: 1 4 5 6
Week: 11
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

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.
Objectives: 1 2 3 4 5 6
Week: 15
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Lectures

The lecturer explains the theoretical conceps of the subject with examples. Some complementary materials will be given to the students.
Objectives: 1 2 6
Contents:
Theory
27h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
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.
Objectives: 2 3 4 5
Contents:
Theory
0h
Problems
0h
Laboratory
12h
Guided learning
0h
Autonomous learning
12h

Teaching methodology

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

Evaluation methodology

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.

Bibliography

Basic

  • 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

Previous capacities

None