Exact Approaches for Solving Multiobjective Integer Optimization Problems
Most real-world optimization problems in the areas of applied sciences, engineering and economics involve multiple, often conflicting, goals. In the mathematical model of these problems, under the necessity of reflecting discrete quantities, logical relationships or decisions, integer and 0-1 variables need to be considered. We are then in the context of MultiObjective Integer Programming (MOIP).
MOIP problems combine all the difficulties of both MultiObjective Problems (MOPs) and Mixed Integer Nonlinear Programming problems (MINLPs), which are among the class of theoretically difficult problems (NP-complete). These problems are intrinsically nonconvex and thus require global optimization techniques. Therefore, the design of efficient solution methods is a big challenge for people working in optimization and operations research.
The research activity will be devoted to the study and the definition of new optimization methodologies to deal with specific classes of MOIP problems.