This area is characterized by a wide-range research activity including the study of the mathematical structure of optimization problems, the development of algorithms for their solution, and the implementation and testing of these algorithms.Research topics
APPLICATIONS OF OPERATIONS RESEARCH
Applications on tactical models for Power Production and Energy Market management; Hydrocarbon Products Distribution by ships; Fleet Management.
METHODS AND TOOLS FOR TRANSPORT AND TRAFFIC SYSTEMS MANAGEMENT
OR and AI techniques for measuring, forecasting and controlling transport and traffic systems; Telematics systems for transport and mobility control; Neural networks for traffic modelling and forecasting; Intelligent transportation systems for road traffic management
MIXED INTEGER AND COMBINATORIAL OPTIMIZATION
polyhedral combinatorics; branch-and-cut algorithms: traveling salesman problems; set covering problems; maximum cut in graphs; vehicle routing problems; 0-1 linear programming; mixed nonlinear programming, 0-1 polynomial programming; logic programming; methods for the design of traffic control and expert systems; scheduling and routing problems; exact and heuristic algorithms.
NON LINEAR PROGRAMMING
OPTIMIZATION METHODS FOR DATA MINING
- Large-scale unconstrained and constrained optimization: development of truncated Newton algorithms for the minimization of a nonlinear multivariate function; definition of shifted barrier primal-dual algorithms based on the use of continuously differentiable exact merit function.
- Minimization of quadratic functions and application to least squares problems;
- Derivative-free optimization: definition of methods for unconstrained, linearly and non-linearly constrained problems;
- Global optimization: definition and implementation of "multistart" algorithms for Price-type methods and of "divide the best" type algorithms;
- Methods for Neural Networks and Suppoprt Vector Machine training.
Desing and development of non linear optimization algorithms for the training of Neural Networks and Support Vector Machines; Methods for the extraction of knowledge in logic form from large datasets based on integer programming and the solution of Minimum Cost Satisfiability Problems; integer programming formulation of the Feature Selection problem in high dimensions and implementation of efficient heuristic algorithms for its solution.