Foto del docente

Vittorio Maniezzo

Full Professor

Department of Computer Science and Engineering

Academic discipline: INF/01 Informatics

Director of Organisational Unit (UOS) Cesena of Department of Computer Science and Engineering

Research

Keywords: Computational algorithmics Matheuristic algorithms Data analytics Predictive analytics Prescriptive analytics Geographic Information Systems

The scientific sector of major interest for Vittorio Maniezzo is computational algorithmics. Within this sector, the main research topics of current interest for me are:
1) mathematic programming methods for matheuristic design.
I am convinced of the opportunity of introducing mathematic programming elements into metaheuristic frameworks, both into current ones and into new ones to be designed for this purpose. The elements that I believe of greater interest are the bound to the cost of an optimal solution an dual variable costs. Following this research line, I first developed a specific variant of the Ant Colony Optimmizaton metaheuristic (ACO, an approach I contributed originally design), then I started a series of international workshops, named matheuristics, dedicated to the possibility of using mathematic programming elements inside metaheuristic contexts.

2) Decision Support Systems and Geographic Information Systems
I am extremely interested to the complete cycle of the optimizaton process: analysis of real-world problems, modelling, design of solution techniques, validation of computational results. The sector where I mainly had the opportunity to deploy optimizaton systems which were validated on real world problems (i.e., decision support systems) is computational logistics. Geographic Informatoin Systems are a precondition to the possibility of working on computational distribution logistics. The study of this sector led me to the design of a customized open-source GIS, named Ertha.

Further research topics of current or past interest for me are:
- naturally inspired algorithms, ant colony optimizaton (ACO);
- production scheduling, project scheduling;
- cognitive modelling, psychogenetic learning;
- cellular automata for dynamic processes simulation (traffic flows, pollutant dynamics).