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
Recognize the main components of a decision making problem and decide the most appropriate modelization method.
Related competences:
CEA12,
CG3,
CEP3,
Build a preference model according to the heterogeneous data types.
Related competences:
CEA12,
CT7,
Make an appropriate selection and use of aggregation operators.
Related competences:
CEA12,
CEP3,
Study and apply methods based on the Multi-Attribute Utility Theory.
Related competences:
CEA12,
CEP3,
CT4,
CT7,
Study and apply methods based on Outranking models for MCDA.
Related competences:
CEA12,
CEP3,
CT4,
CT7,
Identify the relations between MCDA (Multi-criteria Decision Aiding) and AI (Artificial Intelligence)
Related competences:
CEA12,
CEP3,
Contents
1 Introduction
1.1 The decision making problem. Formalization.
1.2 MCDA applications
2 Preference representation models
2.1 Data types
2.2 Family of criteria
2.3 Uncertainty
3 Multi-Attribute Utility Theory
3.1 Introduction
3.2 Steps: aggregation and exploitation.
3.3 Aggregation operators. Properties.
4 Models based on outranking relations
4.1 Introduction
4.2 Outranking relations
4.3 ELECTRE
5 MCDA and AI
Use of MCDA in combination with other intelligent techniques, like intelligent recommender systems.
Activities
ActivityEvaluation act
Exam
Final exam with questions and exercices Objectives:12345 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:1456 Week:
11
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
2h
Autonomous learning
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. Objectives:123456 Week:
15
Theory
0h
Problems
0h
Laboratory
1.5h
Guided learning
0h
Autonomous learning
9.5h
Lectures
The lecturer explains the theoretical conceps of the subject with examples.
Some complementary materials will be given to the students. Objectives:126 Contents:
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:2345 Contents: