Overall objectives:

1 - To know some optimization models and methods with applications to various real problems, such as in transportation, logistics, manufacturing, project management, finance, among others.
2 - To know generically many of computational method and their basic mathematical ideas for solving optimization problems.
3 - To have a deeper knowledge of an optimization problem and its computational resolution previously studied.
4 - To know how to use a software tool (MATLAB and/or Excell) for numerical computation and graphical display.

Syllabus:

1 - Examples of optimization problems with applications to different areas.
2 - Comparative analysis between: static vs dynamic optimization; deterministic vs stochastic optimization; unconstrained vs constrained optimization; continuous vs combinatorial optimization; local vs global techniques; exact vs heuristic optimization.
3 - Some particular optimization problems, as instance: minimum cost maximum flow; project management with PERT and CPM; dynamic programming; line search methods for unconstrained optimization; Lagrangian methods for constrained optimization.

Literature/Sources:

M. S. Bazaraa, J. J. Jarvis, H. D. Sherali , 1990 , Linear Programming and Network Flows , John Wiley & Sons
W. L. Winston , 2004 , Operations Research: Applications and Algorithms , Tomson Brooks/Cole
M.S. Bazaraa, H.D. Sherali, C.M. Shetty , 1993 , Nonlinear Programming: Theory and Algorithms , John Wiley & Sons

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
Explanatory lectures with problem solving on the whiteboard. Some lectures using the computer for solving a particular problem. Evaluation: two individual tests, with equal weights (40% each). One individual mini-work, with weight 20%.