Faculdade de Ciências e Tecnologia

Decision Support Models



Academic unit

Faculdade de Ciências e Tecnologia


Departamento de Matemática



Teacher in charge

Maria Isabel Azevedo Rodrigues Gomes

Weekly hours


Teaching language



- Introduce basic Decision Theory definitions;

- Present several different models used in Decision Support Systems;

- Introduce students to problems related to the subjectivity of Decision Making and how different methodologies handle those problems;

- Facilitate the students'''''''''''''''' contact with quasi-real Decision Making Processes by exposing them to small Case Studies. These Case Studies are usually inspired by real situations.

- Generalize Linear Programming to Multi-Objective approaches;

- Present several methods for finding Efficient Solutions in MOLP problems.


Although not fundamental, previous knowledge of  Linear Programming is recommended.

Subject matter

1 – One criterion decision:

            Decision and Uncertainty;

            Decision and Risk;

            Sequential Decisions and Decision Trees;

            Utility Theory;

            Markov Decision Models;


2 – Multi Criteria Decision:

            Compensatory Models – SMART and TOPSIS Techniques;

            Non-Compensatory Model – ELECTRE Methodology;

            Hierarchic Models – AHP.


3 – Multi Objective Optimization:

            Solutions and Objectives. Dominance and Efficiency;

            Aggregated Sums Models;

            Weight Vectors Models;

            Change of Scale;

            Reduction of Feasible Region;

            Goal Programming;

            Interactive Models: STEM.


Hillier, Lieberman, Introduction to Operations Research, Mc Graw - Hill, 10th ed (2015) - or any other edition

Ruy A. Costa, "Elementos de apoio às aulas de Investigação Operacional (B)", "Enunciados de Exercícios de Investigação Operacional (B)"

Goodwin, P. e Wright, G. – Decision Analysis for Management Judgement (2014 - 5ªed) – John Wiley & Sons

Anderson et al – Quantitative Methods for Business (2001) – SW College Publicating

Saaty, T. L.– The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation (1990) – RSW Publications

Steuer, R. E.– Multiple Criteria Optimizations: Theory, Computation, and Application (1986) – John Wiley & Sons

Teaching method

In each 4 hour lesson a new topic is presented and the students explore it by studying and solving a related Case Study.

The proposed solutions are discussed by the classe and corrected.

Lessons are held on a computational lab.


Evaluation method

A student has to attend to a minimun of 2/3 of held lessons. Otherwise, the student will be excluded from evaluation and will fail.

During the semester there will be two 90-minutes mid-term tests (graded 8 points each) and a group project (graded 4 points).

Being CTi the grade of Test i and CT the project grade, a student will succeed if  CT1 + CT2 +CT>= 10. 

A student who fails on the mid-terms tests can try to succeed on the Final Examination.

 Let CE be the grade on the Examination. A student will succeed if  CE >= 10.