Ricerc@Sapienza

Many interesting and challenging real world problems can be modelled as optimization problems where the relationships between the variables and the values of the objective functions and some constraints are extremely complex and cannot be described analytically. Such class of problems need the use of efficient simulation techniques able to provide sufficiently good approximations of the behaviors of the objective functions and constraints to vary the variables of the problems.
The research activity will be devoted to the study and the description of new methodologies and algorithms for solving the previous simulation based optimization problems. This activity will be carried on along two main themes.
The first one is the definition of new algorithms and strategies for tackling difficult classes of simulation based optimization problems such as constrained global multiobjective optimization problems, nonlinear mixed optimization problems, nonsmooth optimization problems, problems where the objective function and the constraints can be approximated with different precisions, bilevel optimization problems, stochastic optimization problems.
The second theme focuses on the need of defining new optimization methods for the realization of new approximation/simulation tools. This need follows from the fact that, recently, particularly complex problems must be considered. The available simulation codes are not able to describe these problems. This difficulty could be overcome by using data mining tools efficiently trained by new and more efficient optimization techniques.
From the practical point of view, the new algorithms developed by the two research activities will be used for tackling difficult real problems deriving from optimal designs of electrical motors, optimal designs of electrical magnetic apparatus, optimal ship design problems, managements of healthcare services, workforce management, definitions of optimal trading strategies.