Laboratorio di Ricerca: Global Optimization Laboratory (GOL)
Via di S. Marta 3, 50139 Firenze
Dipartimento di Ingegneria dell’Informazione (DINFO) - University of Florence

Marco Roma

PhD Student

I was born in Codigoro, near the city of Ferrara, in Italy. I grew up with a strong interest in different subjects and culture in general, building up a strong curiosity, especially for the scientific and technical field, and an interest in dissemination activities. I graduated from the University of Ferrara in 2021 with a Master's degree in Information Engineering. During my thesis, I developed a passion for optimization and operation research. I am currently a Ph.D. student of the "Smart Industry" curse at the University of Pisa.


Operation Research; Optimization; Health Care Systems Engineering

Research Topic:

My research is focused on optimization for Health Care systems, and is divided into 2 main topics: one is devoted to the development of a decision support tool able to help the management of Care Pathways of chronic patients; the second addresses the problem of generating feasible timetables for a surgical team working at a hospital network, considering fairness related aspects. The main idea concerning the first work is to exploit the amount of requests of the patients, that is known in advance in order to centralize the scheduling process, with the goal of improving the use of shared resources, whose availability is limited and planned by the hospital. In order to achieve this goals, we have to deal with the complexity the problem. For this reason we are developing a Logic-Based Benders decomposition approach and an hybridization of different optimization tools. The second work concerns a well know scheduling problem, but the challenging part is the introduction of fairness aspects. In fact, beside complying with the feasibility constraints to cover all the service taking into account the legal limits imposed and the different contracts, we have to consider individual and management preferences and soft constraints, involving different stakeholders. Moreover, since the team members have very diversified sets of qualifications, a rotation over the shifts is not viable. In order to achieve this goal, we are developing an approach where, for each assignment of a physician to a task, we compute a weight dependent on the past number of assignments. The system will promote the assignment of a task to physicians whose previous workload is lower, and discourage the assignment to those who have already an high previous workload.