The study of possible nuclear fleet evolution is done through scenario calculations. A scenario models precisely all material flows within the fuel cycle, starting with raw material extraction, following with fuel fabrication, fuel irradiation inside the reactor, spent fuel cooling, fuel reprocessing and waste disposal. The scenario is thus a great tool for decision making. However, a scenario is really dependant on the set of hypotheses considered, that are affected by deep uncertainties. The current way to perform scenario calculation is not well suited to manage such hypotheses changes due to uncertainties.

A new field of research has emerged to deal with these deep uncertainties : the study of scenario robustness and resilience. The objective is no longer to quantify the performances of a precise scenario, but its ability to be modified to answer to the objective or constraint change (such as an installed power variation). To do so, it is necessary to launch several thousands of calculations, among which a large part are not viable.

The goal of this thesis work is to investigate the optimization methods used in logistics in order to build efficient methods to quickly build scenario inputs. The generated inputs should lead to optimal scenarios for a set of given objectives. Then, it would be possible to identify the scenarios that are able to answer to several objectives and assess whether they can be adjusted to answer to new constraints. In other words, this thesis is another step towards the production of resilient scenarios against future uncertainties.