IDENTIFYING DATA 2015_16
Subject (*) MULTI-CRITERIA DECISION SUPPORT SYSTEMS Code 17665210
Study programme
Computer Engineering: Computer Security and Intelligent Systems (2013)
 Cycle 2nd
Descriptors Credits Type Year Period
4.5 Optional 2Q
Language
Anglès
Department Enginyeria Informàtica i Matemàtiques
Coordinator
VALLS MATEU, AÏDA
 E-mail aida.valls@urv.cat
Lecturers
VALLS MATEU, AÏDA
Web
General description and relevant information 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).

Competences
Type A Code Competences Specific
 A1 Project, calculate and design products, processes and installations in all areas of computer engineering.
 A3 Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in all areas related to computer engineering.
 D1 Integrate the fundamental technology, applications, services and systems of computer engineering, in general, and in a broader, multidisciplinary context.
 T5 Analyse the information needs considered in an environment and execute all stages of the construction process of an information system.
 T9 Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
Type B Code Competences Transversal
 B2 Aplicar el pensament crític, lògic i creatiu, demostrant capacitat d’innovació.
 B3 Treballar de forma autònoma amb responsabilitat i iniciativa.
Type C Code Competences Nuclear
 C2 Be advanced users of the information and communication technologies
 C3 Be able to manage information and knowledge
 C5 Be committed to ethics and social responsibility as citizens and professionals

Learning outcomes
Type A Code Learning outcomes
 A1 Integrate theoretical knowledge into the realities to which it may apply.
 A3 Apply the techniques learned in a specific context.
 D1 Analyse the problems and their causes from a global focus in the medium and long term.
 T5 Identify the components of a decision-making problem and know how to decide the most suitable decision-making model.
 T9 Design technology to guarantee privacy for scenarios of IT and telematics applications.
Type B Code Learning outcomes
 B2 Identify things that need to be improved in complex situations and contexts.
Apply innovative techniques and obtain results.
 B3 Take correct decisions at key moments confidently, consistently and systematically.
Type C Code Learning outcomes
 C2 Understand the operating system as a hardware manager and the software as a working tool.
 C3 Locate and access information effectively and efficiently.
 C5 Respect fundamental rights and equality between men and women.

Contents
Topic Sub-topic
1. Introduction 1.1 The decision making problem. Formalization
1.2 MCDA applications
2. Preference representation models 2.1 Numerical data types
2.2 Categorical data types
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.

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
2 0 2
Lecture
B2
 20 20 40
Presentations / expositions
A1
T5
C2
C3
 6 29.5 35.5
Practicals using information and communication technologies (ICTs) in computer rooms
A3
T9
C2
 10 10 20
ICT practicals
D1
B3
C5
 3 6 9
Personal tuition
2 0 2
 
Objective short-answer tests
A3
B2
 2 2 4
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities Presentation of the teachers, the goals of the course and the evaluation procedure.
Lecture Each week some lectures will be given, with materials available in advance in Moodle.
Presentations / expositions The student will collect research papers about a certain topic (proposed by the teacher) and will make an overview and comparison. The resulting report will be evaluated. The work will also be explained in an oral presentation to the rest of students.
Practicals using information and communication technologies (ICTs) in computer rooms Students will solve practical case studies using public software tools, or programming some algorithms.
ICT practicals The student must solvesome exercises in class (in the lab). A report will be delivered by the student, in some exercises.
Personal tuition The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly.

Personalized attention
Description
The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly.

Assessment
Methodologies Competences Description Weight        
Presentations / expositions
A1
T5
C2
C3
 Oral exposition of a research work. 30%
Practicals using information and communication technologies (ICTs) in computer rooms
A3
T9
C2
 Solving exercises in class (in the lab). Participation in class will also be considered.
 10%
ICT practicals
D1
B3
C5
 Student will solve practical case studies using public software tools, or programming some algorithms.
 20%
Objective short-answer tests
A3
B2
 Final exam. A minimum qualification of 5 is required to pass the course.
 40%
Others  
 
Other comments and second exam session

Sources of information

Basic Figueira, J., Greco, S., Ehrgott, M (eds), Multiple Criteria Decision Analysis, Springer, 2005
Doumpos, M., Grigoroudis, E., Multicriteria Decision Aid and Artificial Intelligence, Wiley, 2013

Complementary http://www.cs.put.poznan.pl/ewgmcda/, Euro working group on MCDA, ,
http://www.mcdmsociety.org/, Int Society on MCDM, ,
Matthias Ehrgott, José Rui Figueira and Salvatore Greco, Trends in Multiple Criteria Decision Analysis, Springer, 2010
Torra, V., Narukawa, Y., Modelling Decisions: Information fusion and Aggregation operators, Springer, 2005

Recommendations
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.