Optimization and Decision Support Methodologies

Before attending a curricular unit, students must have obtained approval for the Operational Research course (2nd year of this degree).

It is used a learning strategy supported in experiments of subjects outlined in lecture, either by solving exercises on the topics addressed either through the implementation of some algorithms.
a)   In the theoretical component oral presentation of the subject will be made, using chalkboard and Power-Point projections (it is encouraged the active participation of students through the placement of simple questions).
b)   On theoretical-practical component students solve application exercises of the theoretical component.
c)   In-laboratory practical component students implement some methodologies addressed in the theoretical component.
It is necessary an individual study of students out of class (for better monitoring of lessons)

The assessment consists of final individual written test on the topics taught.
The exams are essentially practical, and the approval requires a grade greater than or equal to 10 ([0, 20]).

Based on the concepts acquired in Operations Research, in this curricular unit students expand their knowledge in optimization and decision support areas. Accordingly, will be introduced methodologies to apply to more complex decision problems than those previously studied, as is the case of problems involving multiple objectives, integer variables, etc..
After attending this curricular unit, students must:
1   – Know and understand the fundamental characteristics of the most representative optimization and decision support problems
2   – Identify different approaches that can be used to solve them.
3   – Solve simple practical problems using the appropriate optimization and decision support algorithms and interpret the obtained solution(s).
4   – Be able to implement computationally some of the optimization and decision support algorithms.

Theoretical content:
1.   The linear programming model (revisions)
2.   Integer linear programming
3.   Multi-objective linear programming
4.   Goal Programming
5.   Dynamic programming

Theoretical-practical/practical content:
–   Resolution of theoretical-practical exercises involving the various chapters of the theoretical program
–   Computational implementation of algorithms

NAO