  Many real-world decision-making problems involve multiple decision-making
stages and various objectives. Besides, most of the decisions need to be made
before having complete knowledge about all aspects of the problem leaves some
sort of uncertainty. Deep uncertainty happens when the degree of uncertainty is
so high that the probability distributions are not confidently knowable. In
this situation, using wrong probability distributions leads to failure.
Scenarios, instead, should be used to evaluate the consequences of any
decisions in different plausible futures and find a robust solution. In this
study, we proposed a novel multi-stage multi-scenario multi-objective
optimisation framework for adaptive robust decision-making under deep
uncertainty. Two approaches, named multi-stage multi-scenario multi-objective
and two-stage moving horizon, have been proposed and compared. Finally, the
proposed approaches are applied in a case study of sequential portfolio
selection under deep uncertainty, and the robustness of their solutions is
discussed.
