Decision Support Models
Code
8416
Academic unit
Faculdade de Ciências e Tecnologia
Department
Departamento de Matemática
Credits
6.0
Teacher in charge
Maria Isabel Azevedo Rodrigues Gomes
Weekly hours
4
Teaching language
Português
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.
Prerequisites
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.
Bibliography
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 OR
Have a grade equal ou greater than 3 on 80% of weekly homeworks
If none of these conditions is verified, the student will be excluded form evaluation and fail.
During the semester there will be three 60 minutes mid-term tests (graded between 6 and 7 points each).
Being CTi the grade of Test i, a student will succeed if CT=CT1 + CT2 + CT3>= 9,5. The Final Grade will be the rounding of CT.
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 >= 9,5. The Final Grade will be the rounding of CE.