Healthcare Optimization | Intelligent Optimization Lab

Francesca Da Ros Wins the 2026 AIROYoung Dissertation Award

Thu, 10 Sep 2026 00:00:00 +0000

IOLab congratulates Francesca Da Ros on winning the 2026 AIROYoung Dissertation Award with her thesis, Chronicles on Metaheuristic Optimization: Components, Applications, and Large Language Models. The award ceremony took place on 10 September 2026 during the national ODS conference in Galzignano Terme, Italy.

The AIROYoung Dissertation Award is presented by the Italian Operations Research Society to the best doctoral dissertation in Decision Sciences defended at an Italian university during the relevant period. The selection process evaluates both the dissertation and its related scientific publications and concludes with a presentation by the finalists at the ODS conference.

Francesca Da Ros at the AIROYoung Dissertation Award ceremony during ODS 2026.

Francesca’s research investigates advanced optimization techniques for complex decision-making, with a strong focus on real-world applications. One case study addresses the scheduling of production cycles for industrial furnaces, seeking to improve production efficiency while reducing energy consumption and making the process more sustainable.

A second application concerns home healthcare planning. Optimization methods are used to organize caregivers’ visits and activities while considering not only service efficiency, but also individual patient needs and the personalization of care.

Alongside these applications, the dissertation examines how the behavior of optimization algorithms can be better understood and compared. It also investigates how generative artificial intelligence systems, particularly large language models, can support optimization processes.

IMHO

Sat, 01 Jan 2022 00:00:00 +0000

IMHO — Models and algorithms for the optimization of integrated healthcare management was funded by the Italian Ministry of University and Research under the PRIN 2020 programme and ran from 2022 to 2024.

The project developed optimization models and algorithms for coordinating healthcare services and resources as an integrated system. IOLab contributed expertise in scheduling, timetabling, metaheuristics, and decision support, with the aim of improving operational efficiency while accounting for the needs of patients and healthcare professionals.

Healthcare Optimization

Tue, 01 Jun 2021 17:03:33 +0200

IOLab develops optimization methods for coordinating healthcare services and resources while accounting for operational efficiency, quality of care, and the needs of patients and healthcare professionals.

Our work includes patient admission scheduling, where patients are assigned to beds while considering clinical requirements, comfort, emergency arrivals, uncertain lengths of stay, and the possibility of delaying admissions. We also study home healthcare routing and scheduling, jointly planning caregivers’ routes, visits, skills, continuity of care, and patient preferences.

In emergency medicine, the group collaborated with ASUFC within EasyNet to build a data-driven simulator of the Friuli Centrale emergency medical system and optimization models for ambulance location and relocation.

These strands were brought together in IMHO, a PRIN project on models and algorithms for integrated healthcare management. The group publishes benchmark instances, generators, validators, and reference methods through the UHHC repositories.

EasyNet

Mon, 01 Jun 2020 00:00:00 +0000

Within EASY-NET, IOLab collaborated with ASUFC on the data-driven analysis and redesign of the Friuli Centrale emergency medical service.

The group developed a simulator for evaluating emergency scenarios and optimization models for locating and relocating ambulances across the regional service area. The work supports audit and feedback activities by connecting operational data with quantitative evaluation of alternative configurations.

The project combines data analytics, simulation, location models, and optimization to help decision makers assess service coverage and resource allocation before introducing changes in the field.