Advanced Topics of Operational Research

Recommended Prerequisites

Knowledge about optimization models and algorithms. Domain of a programming language.

Teaching Methods

Seminars and tutorial guidance sessions to be held with each student, on the syllabus presented above.

Once the basic competences are acquired, these sessions will be oriented according to the specific work to be developed by each student, which will be defined to be useful for the PhD thesis.

Learning Outcomes

Provide the students advanced methodological competences in operational research, with focus on meta-heuristic approaches to deal with complex optimization problems (of combinatorial nature and multiple objective functions).  Present illustrative examples of engineering problems to be dealt with these techniques.

Work Placement(s)

No

Syllabus

Meta-heuristics in complex combinatorial and nonlinear optimization problems. Tabu search. Simulated annealing. Genetic/evolutionary algorithms. Particle swarm optimization. Differential evolution. Applications in engineering problems.

Head Lecturer(s)

Carlos Alberto Henggeler de Carvalho Antunes

Assessment Methods

Assessment
The evaluation elements are a detailed report describing the computational implementations of the algorithms and the corresponding comparative analysis, as well as a scientific paper desirably to be submitted to an international scientific conference or journal: 100.0%

Bibliography

- Z. Michalewicz, D. B. Fogel. "How to Solve It: Modern Heuristics", Springer, 2004.

- E. Talbi. “Metaheuristcs - from design to implementation”, Wiley, 2009.

- R. Takahashi, A. G. Cunha, C. H. Antunes. “Manual de Computação Evolutiva e Metaheurística”, Imprensa da Universidade de Coimbra, 2012.

 

Outra bibliografia será definida nas sessões tutoriais de acordo com o problema a resolver e as técnicas algorítmicas a aplicar. 

 

Other bibliography will be defined in the tutorial sessions according to the problems to be tackled and the algorithmic techniques to be applied.