Operations Research consists in building up rational models for the representation of complex problems and in devising the related solution algorithms. Objective of the course is to deepen the student skills on several major topics of operations research with emphasis on computational complexity, combinatorial optimization and multiobjective optimization.

At the end of the course the student must be able to determine the computational complexity of given algorithms, to design exact and heuristic algorithms for real world combinatorial optimization problem and/or to use software tools when dealing with more complex problems.

Bases of operations research with emphasis on linear programming

Computational complexity.
Elements of graph theory.
Combinatorial optimization: exact methods, heuristic approaches, approximation algorithms.
Multiobjective optimization: pareto-optimality, solution approaches.
Case studies on location, routing, scheduling and timetabling problems.

The exercise classes are related to the topics presented in the course. In the laboratories commercial softwares are used to solve practical case studies.

Slides will be provided directly by the teacher.
Reference textbooks that contain part of the topics presented in the course are among others
• M.R. Garey, D.S. Johnson (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Co.
• C.H. Papadimitriou, K. Steiglitz (1982), Combinatorial Optimization. Algorithms and Complexity, Prentice Hall.
• V. Vazirani (2001), Approximation Algorithms, Springer.
• P. Vincke (1992), Multicriteria decision-Aid, Wiley, Chichester.
• L.A. Wolsey (1999), Integer Programming and Combinatorial Optimization, Wiley.

Written test.