- Unit Coordinator: Lluis Alseda Soler, Martin Hernan Campos Heredia, Judit Chamorro Servent, Susana Serna
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Autonomous University of Barcelona
- Language: English
- Aims:
Analysis of case studies, and practice of team working dynamics and client-consultant relationship.
The cases will focus primarily on optimisation and logistics real problems, but may have a scope beyond the pure "decision making" setting.
The course will be organised and directed by a professor of the department of Mathematics and will include minicourses and presentations given by industrial collaborators and other departments' teaching staff.
- Content:
Mathematical modelling, i.e. solving real-world problems by means of mathematics.
- Pre-requisites:
Students must have mathematical and computational skills at the level of a science degree.
- Reading list:
- Ch. Rousseau, Y. Saint-Aubin, 2008. Mathematics and Technology. Springer.
- P. Pevzner, R. Shamir, 2011. Bioinformatics for Biologists. Cambridge Univ. Press
- Unit Coordinator: Herbert Steinrück
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Vienna University of Technology
- Language: English
- Pre-requisites:
- Partial differential equations,
- Fluid mechanics,
- Numerical methods for fluid mechanics
- Reading list:
- Lecture notes,
- P. G. Drazin, Introduction to Hydrodynamic Stability, Cambridge University Press, Cambridge (2002)
- S. Chandrasekhar, Hydrodynamic and Hydromagnetic Stability, Oxford University Press (1961) P. G. Drazin, Introduction to Hydrodynamic Stability, Cambridge University Press (2002)
- Unit Coordinator: Ekaterina Shulman
- ECTS Credits: 3
- Semester: 2
- Year: 2
- Campus: University of Silesia in Katowice
- Language: English
- Aims:
In this module the students, divided into teams consisting of several people, implement projects associated with the given problem.
- Content:
The project consists of several phases:
1. Planning for the project. The allocation of roles and responsibilities in the team.2. Review of available literature on the given matter.
3. Analysis of the problem, seeking methods of its solution.
4. Implementation of the solution. This phase, depending on the project, should include elements such as the analysis of empirical data, calibration, simulation and testing of the solution.
5. Preparation of the final report and presentation of results. Both the final effect and the individual phases of the project are assessed. Laboratory classes serve to current reporting and discussing work progress, and give the opportunity of obtaining assistance in the project implementation.
- Unit Coordinator: Albert Ruiz Cirera, Judit Chamorro Servent
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Autonomous University of Barcelona
- Language: English
- Aims:
The objective of this course is to study and practise the paradigmatic problems (Routing, Scheduling, Location, Packing), relevant in logistics, that lead to discrete and combinatorial optimisation models, intrinsically difficult in practice due to its huge input size.
The modern metaheuristics for approximating the solutions of such problems, like Evolutionary Algorithms, Tabu Search, Particle Swarm, or Ant Colony will be introduced
- Content:
- Combinatorial Algorithms for graphs and routing: Dijstra and A* algorithms.
- Optimisation on graphs.
- Deterministic optimization for nonlinear problems (constrained and non-constrained).
- Genetic Algorithms.
- Simulated Annealing.
- Ant colony optimisation algorithms.
- Particle swarm optimization.
- Neural Networks in optimization.
- Scheduling.
- Machine learning trough neural networks.
- Pre-requisites:
Mathematical knowledge at the level of Science degree. Programming skills
- Reading list:
- Judea Pearl, A* Algorithms and such: Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, 1984.
- Alfio Quarteroni, Riccardo Sacco, Fausto Saleri, Numerical Mathematics, , Texts in Applied Mathematicsm 37, Springer, 1991.
- Sean Luke, Essentials of Metaheuristics, 2009: http://cs.gmu.edu/∼sean/book/metaheuristics/
- David Beasley, David R. Bully and Ralph R. Martinz, An Overview of Genetic Algorithms (Part 1: Fundamentals and Part 2: Research Topics)
- S. Kirkpatrick, C. D. Gelatt Jr. and M. P. Vecchi, Optimization by Simulated Annealing, Science, May 1983, Vol. 220, no. 4598, pp. 671-680.
- William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery, Numerical Recipes in C.
- The Art of Scientific Computing (second edition)}, Cambridge University Press.
- Marco Dorigoa and Christian Blum, Ant colony optimizationtheory: A survey, Theoretical Computer Science 344 (2005) 243 - 278.
- Ronald L. Graham, Combinatorial Scheduling Theory
- R. Gary Parker, Deterministic Scheduling Theory, Chapman Hall.
- Peter Brucker, Scheduling Algorithms, Fourth Edition, Springer
- R.L. Graham, E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Khan, Optimization and approximation in deterministic sequencing and scheduling: a survey
- Peter Brucker, Scheduling Algorithms, Springer-Verlag, 2007, Berlin Heidelberg New York (ISBN 978-3-540-69515-8).
- Jean-Yves Potvin, Kate A. Smith, Artificial Neural Networks for Combinatorial Optimization
- Kate Smith, Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
- Kate Smith, Marimuthu Palaniswami and Mohan Krishnamoorthy. Neural Techniques for Combinatorial Optimization with Applications
- Lecture notes provided by the teacher.
