– French National Center for Scientific Research (CNRS), France
– Department of Computer Science, UTC – Sorbonne University, France
Multiband Robust Optimization: theory and applications
Over the last years, Robust Optimization (RO) has emerged as an effective and efficient
methodology to tackle data uncertainty in real-world optimization problems. RO takes into
account data uncertainty in the shape of hard constraints that restrict the feasible set and
maintain only robust solutions, i.e. solutions that remain feasible even when the values of the
input data change.
In this talk, I will present an overview of my research about theory and applications of RO.
Specifically, I will provide an introduction to Multiband Robustness (Büsing and
D’Andreagiovanni 2012), a model for RO proposed to generalize and refine the classical
Gamma-robustness model by Bertsimas and Sim. The main aim of Multiband Robustness is
to provide a refined representation of arbitrary non-symmetric distributions of the uncertainty,
which are commonly present in real-world applications. Such refined representation grants a
reduction in conservatism of robust solutions, while maintaining the accessibility and
computational tractability that have been a key factor of success of Gamma-robustness. I will
also provide an overview of applications of the Multiband model to real-world problems that I
have considered in past and ongoing research and industrial projects.
Seminar: Fabio D’Andreagiovanni on “Multiband Robust Optimization: theory and applications”