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Gianmaria Leo

Gianmaria Leo

Gianmaria Leo

Senior Operations Research Engineer of the IBM Data Science Elite Team

Gianmaria is a Senior Operations Research Engineer of the IBM Data Science Elite Team. He has over 7 years of experience with the design & development of innovative solutions and prototypes based on Operations Research, Optimization and Analytics. As Senior Data Scientist, he leads Proof-of-Concept and projects with IBM customers worldwide, focusing on Energy and Banking among other industries.

Before joining IBM, he worked for Sabre Airline Solutions as Principal Operations Research Consultant. He developed prototypes and delivered projects to strategic customers in both the fields of Airline Operations and Revenue Management. In the Airline Operations area, he designed and prototyped the optimization engine of a real-time system providing optimized recommendations and situation awareness to airline’s Hub Control Center. In the Revenue Management area, he developed projects and solutions based on Network Planning, Pricing and Inventory Controls Optimization. Before joining Sabre, Gianmaria worked for Bombardier Transportation. He contributed to the design and implementation of the optimization core of a real-time system, which provides optimal train dispatching decisions over controlled railway lines and stations.

Gianmaria holds a PhD in Operations Research from “La Sapienza” University of Rome. His PhD focused on theoretical and computational aspects of Integer Programming, Combinatorial Optimization and Graph Theory. After the PhD studies, he was a research collaborator at the University of Rome “Tor Vergata” and the University of Bologna. He contributed to projects between Industry and Academia in Healthcare, Aerospace, Defense and Telecommunications.

Track: Risk Management & Predictive Analytics

Monday, April 15, 9:10–10:00am

Increasing Economic Sustainability of Electric Power Planning Under Uncertainty

The electric power planning is a critical decision-making process, which aims to achieve the right trade-off between safety, continuity of energy supply and sustainability. This business practice is often challenging, since uncertain demand and operational conditions have remarkable impact. The problem often becomes more complex with the presence of renewable sources: increased risks of supply disruption or energy spillage often arise due to the high variability of renewable generation.

Our work focuses on a system serving a restricted isolate electric grid, managed by a major European electricity provider. Our Predictive-Prescriptive pipeline supports the entire process. We introduced a Robust Optimization approach reducing costs while improving sustainability. We compared this new approach with more typical solutions adopted in production, by performing an ex-post analysis of different planning recommendations over twenty days of operations. The introduced Optimization model is computationally effective, providing high-quality daily plans in less than one second.