Decision Support Models


- 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.

General characterization





Responsible teacher

Paula Alexandra da Costa Amaral


Weekly - 4

Total - 4

Teaching language



Available soon


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

Goodwin, P. e Wright, G. – Decision Analysis for Management Judgement (2014 -  5th ed.) – 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

Classroom classes

Evaluation method

Approval for this discipline can be obtained by continuous evaluation or by final exam.Continuous evaluation comprises: 

1 . 2 assignments (1 in group (10%) and 1 individual (20%), both mandatory ; 

2 -  written test (60%) must have at least 7 points;

3 - Participation in the classroom (10%).

For the final exam, the final classification will be given based exclusively on exam.

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.