Teaching Methodologies
The teaching activity takes place in the classroom or via videoconference, with the exposition of concepts, techniques and methods, with a
strong focus on practical applications. Software will be used to support the resolution of larger problems. The classes are performed with
computer support, encouraging the practical application of algorithmic knowledge.
Learning Results
This subject introduces techniques to support the decision-making process, using linear and integer linear programming models, including
network optimization.
Techniques for solving the proposed mathematical models will be studied and their application within Management be encouraged.
Appropriate software will be used to solve the proposed optimisation models, including Python libraries.
The course also focuses on the study of algorithms for Artificial Intelligence, including heuristic and metaheuristic techniques for solving
combinatorial optimization problems, with special attention to their practical application.
During the course, several Management problems will be raised, through which it is intended to create in the student sensitivity to the
mathematical modelling of such problems, as well as their algorithmic resolution. In this way, it is intended to establish bridges aimed at the
use of quantitative analytical techniques to support the decision-making process.
Program
1 Introduction to mathematical modelling
1.1 Mathematical formulation of problems
1.2 Application cases to Management
2 Linear Programming
2.1 Properties
2.2 Techniques and software for solving continuous linear models
2.3 Sensitivity analysis and economic interpretation of solutions
3 Network Optimization
3.1 Graphs/networks concepts and properties
3.2 Transportation and allocation
3.3 Shortest path problem
3.4 Maximum flow problem
3.5 Minimum cost flow problem
4 Integer programming
4.1 Properties of linear formulations with integer variables
4.2 Modelling techniques using binary variables
4.3 Using computational tools to solve integer linear models
5 Heuristics and Metaheuristics in Artificial Intelligence
5.1 Approximate techniques for solving combinatorial problems
5.2 Greedy heuristics
5.3 Local search heuristics
5.4 The A* algorithm
5.5 Metaheuristics with deterministic factors and with random factors
Internship(s)
NAO
Bibliography
– E. Costa e A. Simões, Inteligência Artificial. FCA, Lisboa, 2008.
– F.S. Hillier e G.J. Lieberman, Introdução à pesquisa operacional. McGraw Hill Brasil, 2013.
– M.C. Mourão, L. Santiago Pinto, O. Simões, J. Valente e M. Vaz Pato, Investigação Operacional: Exercícios e Aplicações, Dashöfer
Holding Ltd., Chipre, 2011.