Base Knowledge
Basic concepts of linear programming.
Teaching Methodologies
The lectures are expository (using chalkboard and Power-Point projections) but tend to promote the active participation of students, either by asking questions, either through the resolution of exercises involving the application of the topics being exposed.
In practical classes the knowledge acquired in lectures is applied by resolution of exercises.
It is necessary an individual study of students out of class (for better monitoring of lessons).
Learning Results
Goals:
The aim of this curricular unit is to provide students with the minimum concepts necessary to a specialist who can serve as an interface between a Decision Support System (DSS) and the “staff” of a company. On the other hand, the student must be able to computationally implement components of the DSS model subsystem itself.
Based on the concepts already acquired in the Operational Research Curricular Unit, this course will study other types of models that are more complex and closer to reality, such as problems with multiple objectives, dynamic programming problems, etc..
The knowledge acquired can be applied in solving similar algorithms/problems in a real context.
Skills:
After attending this course, students must:
1- Understand the importance of decision support systems and the main concepts related with decision support systems.
2- Identify different types of decision problems and distinct types of optimization methodologies.
3- Identify the suitable algorithm that can be used to solve a simple decision problem.
4- Solve simple practical problems using the appropriate optimization algorithms and interpret the obtained solution(s).
5- Encourage the autonomous study and research work.
Program
• Linear programming with a single objective (revisions)
• Decision linear programming problems with multiple objectives
• Decision linear goal programming problems
• Dynamic programming
Curricular Unit Teachers
Ana Rosa Pereira BorgesGrading Methods
Students can choose four different assessment methods:
1) Final evaluation
a) Final Evaluation (20 points), covering all subjects
2) Periodic/Continuous Assessment,
b) two (2) Evaluation Tests to be carried out during the semester (7 points + 7 points) + Individual research work (6 points)
Or
c) two (2) Evaluation Tests to be carried out during the semester (10 points + 10 points)
Or
d) Final Evaluation (14 points), covering all subjects + Individual research work (6 points)
1 -
The best grade is guaranteed between:
- Final evaluation
- Evaluation Tests
- Evaluation Tests + work
- Final evaluation + work
2 -
- The 1st assessment test will be carried out during academic semester (scheduled for beginning/mid-November).
- The date of the 2nd assessment test (the last) coincides with the date of the last classe.
- The subject matter covered by each assessment tests, will be announced to students (with due advance) during classes.
- A mandatory minimum of 30% is required in each evaluation Test (T1 and T2).
3 -
- Students with final grades of evaluations greater than or equal to 16.5 values to the UC (either obtained by periodic evaluation or by final evaluation) may be subjected to a mandatory validation test at a time to be scheduled.
- The grade validation test will be carried out orally and will cover all the subjects taught at UC.
- Students must inform the teacher if they wish to take the test within 3 days after the final grade is announced, and the test will take place up to 5 days after the final grade is announced.
- If the student does not express that he intends to take the oral test, he will be given a maximum score of 16 points.
Internship(s)
NAO
Bibliography
Main Bibliography:
– Documentation that supports classes (Notes and slides, available on the academic management platform (or in Moodle)
– Clímaco, J. N., Antunes C. H., & Alves M. J. (2003). Programação Linear multiobjectivo: do modelo de programação linear clássico à consideração explícita de várias funções objectivo. Coimbra – Imprensa da Universidade de Coimbra. [Cota ISEC: 3-9-95 (ISEC) – 13356; 3-9-96 (ISEC) – 13392]
– Hillier, F. S., & Lieberman, G. J. (2010). Introduction to Operations Research (6th ed.). New York: McGraw-Hill. [available at ISEC’s Library the 5th edition = Cota ISEC: 3-9-31 (ISEC) – 08145]. The 10th edition (2015) of this book is available for download in: https://pt1lib.org/book/3426899/3de006
– Ramalhete, M., Guerreiro, J., & Magalhães, A. (1984-1985) Programação Linear (vol. I e vol. II) (6a ed.). Lisboa: McGraw-Hill. [Cota ISEC: 3-9-22 (ISEC) V.1º v. – 05262; 3-9-23 (ISEC) V.2º v. – 07016; …]
– Borges, A. R. (2005). Abordagens interactivas para tratamento da incerteza em modelos de optimização multiobjectivo para apoio à decisão. Dissertação de Doutoramento. Faculdade de Ciências e Tecnologia da Universidade de Coimbra. [Cota ISEC: 1A-1-279 (ISEC) – 13507]
Additional Bibliography:
– Taha, H. A. (2007) Operations research: an introduction (8th ed.). London: Prentice-Hall [available at ISEC’s Library the 5th edition = Cota ISEC: 3-9-29 (ISEC) – 07857]
– Steuer, R. (1986) Multiple Criteria Optimization: theory, computation and application. John Wiley & Sons.
– Bazaraa M.S., Jarvis J.J., & Sherali H.D. (2010) Linear Programming and Network Flows (4th ed.), Wiley.