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Mardi 10 Mars
| Heure: |
12:30 - 13:30 |
| Lieu: |
Salle B107, bâtiment B, Université de Villetaneuse |
| Résumé: |
Multiband Robust Optimization: theory and applications |
| Description: |
Fabio D'Andreagiovanni 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, we provide an overview of our research about theory and applications of RO.
Specifically, we present Multiband Robustness, a new model for RO that we recently
proposed to generalize and refine the classical Gamma-robustness model by Bertsimas and
Sim. The main aim of our new model is to provide a refined representation of arbitrary non-
symmetric distributions of the uncertainty, that 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. We also provide an overview of applications of
the Multiband model to real-world problems that we have considered in past and ongoing
research and industrial projects. |
Mardi 24 Mars
| Heure: |
12:30 - 13:30 |
| Lieu: |
Salle B107, bâtiment B, Université de Villetaneuse |
| Résumé: |
Unit Commitment problems: approaches and solution algorithms |
| Description: |
Claudio Gentile The Unit Commitment is a the problem to manage a set of power generating units of different types over a short time horizon.
Unit Commitment is formulated as a Mixed Integer Nonlinear Programming problem. It requires special techniques to handle both the large size of the instances to be solved and the nonlinearity of the objective function.
Two main approaches have been used in its solution: the Lagrangian Relaxation coupled with primal heuristics and MILP approximations. We discuss some new advancements for both approaches. |
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