25 maggio 2026 | 11:00
Dipartimento di Elettronica, Informazione e Bioingegneria - Politecnico di Milano
Sala Conferenze Emilio Gatti (Edificio 20 )
Speaker: Francesca Maggioni (Università di Bergamo)
Contatti: Simone Garatti | simone.garattti@polimi.it
Sommario
Many real world decision problems are dynamic and affected by uncertainty. Stochastic Programming provides a powerful approach to handle this uncertainty within a multi-period decision framework. However, as the number of stages increases, the computational complexity of these problems grows exponentially, posing significant challenges. To tackle this, approximation techniques are often used to simplify the original problem, providing useful upper and lower bounds for the objective function’s optimal value.This talk explores methods for generating bounds for a wide variety of problem structures affected by uncertainty. We begin by discussing bounds based on scenario grouping under the assumption that a sufficiently large scenario tree is given but is unsolvable, both in the context of stochastic programming and distributionally robust optimization. Next, we extend these techniques to address more complex problems, including multi-horizon stochastic optimization.
Finally, the talk introduces the integration of these bounding methods with Benders’ decomposition. To reduce the computational burden of generating a cut for each scenario, a Benders refinement-chain cuts method is proposed, where scenario subsets are used to generate group-wise optimality cuts. This aggregation significantly lowers the number of cuts required, while preserving valid lower bounds. Theoretical relationships between cuts generated at different refinement levels are established. Numerical experiments on various energy and transportation optimization problems demonstrate the efficiency of the proposed approaches.
Biografia
Francesca Maggioni is Full Professor of Operations Research at the University of Bergamo, where she previously served as Assistant and Associate Professor. She graduated in Mathematics from Università Cattolica del Sacro Cuore and obtained a PhD in Pure and Applied Mathematics from the University of Milano-Bicocca. She is currently Deputy Director of the PhD program in Applied Economics and Management (Universities of Bergamo and Pavia) and coordinates the CQIIA-MatNet research group at the University of Bergamo.
Her research focuses on optimization under uncertainty, including stochastic, robust and distributionally robust optimization, with applications in logistics, transportation, energy and finance. She also works on optimization and machine learning models, as well as on mathematical aspects of knotted filaments in physical systems.
She has published more than 65 papers in international peer-reviewed journals in operations research and optimization.
She is Chair of the European Working Group on Stochastic Optimization (EWGSO) and National Coordinator of the AIRO thematic section on stochastic programming. She also serves as Editor-in-Chief of Computational Management Science and Associate Editor for six international journals.
She has given invited talks at international conferences and doctoral schools and has coordinated research projects funded through competitive national programs, including a PRIN 2020 project on urban logistics and sustainable transportation under uncertainty and machine learning. Alongside her research activity, she has organized international conferences, workshops and doctoral schools, and supervised nine PhD students and four postdoctoral researchers.