INTERMATHS EMJM FULL PARTNER

ECTS Credits: 6   |   Semester: 1   |   Year: 2   |   Campus: Autonomous University of Barcelona   |   Language: English

Unit Coordinator: Lluis Alseda Soler, Martin Hernan Campos Heredia, Judit Chamorro Servent, Susana Serna

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

ECTS Credits: 6   |   Semester: 1   |   Year: 2   |   Campus: Autonomous University of Barcelona   |   Language: English

Unit Coordinator: Albert Ruiz Cirera, Judit Chamorro Servent

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.

ECTS Credits: 9   |   Semester: 1   |   Year: 2   |   Campus: Autonomous University of Barcelona   |   Language: English

Unit Coordinator: Juan-José Ramos Gonzalez

Aims:

To deepen in the modelling methods and quantitative techniques aimed to support the decision making process in logistics and supply chain management.

To practice the workflow from the recognition and analysis of a real case problem, to the identification of the feasible activities, and to the selection of good solutions.

Practical problems studied include demand forecasting, production planning, warehouse and inventory management, and transport networks.

Content:

• Introduction to decision making.
• Linear and integer programming, constraint programming, artificial intelligence methods in optimisation.
• Production planning.
• Scheduling problems (job sequencing, resource allocation).
• Heuristics in transport planning.

Reading list:

Main textbooks:

• Hartmurt Stadlert and Cristoph Kilger (Eds.) Supply Chain Management and Advanced Planning. Third Edition. Springer, 2005.
• Ioannis T. Christou. Quantitative Methods in Supply Chain Management. Models and Algorithms. Springer, 2012.
• H. Paul Williams. Model Building in Mathematical Programming. Wiley. 2013 (5th edition)

Further readings:

• Joseph Geunes, Panos M. Pardalos and H. Edwin Romeijn (Eds.) Supply Chain Management: Models, Applications, and Research Directions. Kluwer Academic Publishers, 2002. 
• F. Robert Jacobs, William L. Berry, D. Clay Waybark and Thomas E. Vollmann. Manufacturing Planning and Control for Supply Chain Management. McGraw-Hill, 2011 (6th edition).
• F. Robert Jacobs and Richard B. Chase. Operations and Supply Chain management. McGraw-Hill Irwing, 2011 (13 th edition).

ECTS Credits: 3   |   Semester: 1   |   Year: 2   |   Campus: Autonomous University of Barcelona   |   Language: English

Unit Coordinator: Ramon Anton Piera Eroles

Aims:

To introduce students to the knowledge, processes, skills, tools and techniques suitable for project management, such that the application of them satisfy the requirements specified for project development, and may have a significant impact on its success. Specifically: learning the terminology and basic concepts of project management area and understanding the relationship between logistics and supply chain management and project management.

Content:

• Introduction to Project Management
• System Development Cycle
• Feasibility Study
• Project Planning
• Graphs-based Programming Methods
• Cost Analysis
• Risk Management
• Project Control

Reading list:

• Heagney, Joseph. Fundamentals of Project Management, 5th edition. 2016.
• Martinelli, Russ, et al. Program Management for Improved Business Results, 2nd edition, 2014.
• Nicholas, John M. Project management for business and technology: principles and practice , 2nd edition. Prentice Hall, 2001.
• Nicholas, John M., Steyn, H. Project management for business and technology: principles and practice, 3rd edition. Elsevier, 2008.
• Nicholas, John M. and Steyn, H. Project management for engineering, business, and technology, 4th edition. Routledge, 2012.
• A Guide to the project management body of knowledge: (PMBOK® Guide), 6th edition. Project Management Institute, 2017.
• Lewis, James P. Fundamentals of project management: developing core competencies to help outperform the competition. Amacom, 2002.