# Decision Support Models

## Objectives

- 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

8416

6.0

##### Responsible teacher

Paula Alexandra da Costa Amaral

Weekly - 4

Total - 4

Português

Available soon

### Bibliography

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.

## Programs

Programs where the course is taught: