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.

Industrial Production Scheduling

Sun, 01 Jan 2017 00:00:00 +0000

IOLab develops optimization methods for complex production environments, where scheduling decisions must account for technological constraints, limited resources, energy consumption, and operational objectives.

From 2017 to 2021, the group conducted a research collaboration with Danieli Automation on production scheduling for steel plants. Led by Luca Di Gaspero, the project studied metaheuristic methods for industrial decision-support systems in an Industry 4.0 setting.

More recent work investigates the Oven Scheduling Problem and related parallel-batch scheduling models. The group has developed local-search and large-neighborhood-search methods, as well as instance-space analysis and algorithm-selection techniques for understanding when different algorithms perform best.

Code and experimental data are available through the OSP-LS and OSP-LNS repositories.

Industrial Optimization | Intelligent Optimization Lab

Francesca Da Ros Wins the 2026 AIROYoung Dissertation Award

Industrial Production Scheduling