  Within the mining discipline, mine planning is the component that studies how
to transform the information about the ore resources into value for the owner.
For open-pit mines, an optimal block scheduling maximizes the discounted value
of the extracted blocks (period by period), called the net present value (NPV).
However, to be feasible, a mine schedule must respect the slope constraints.
The optimal open-pit block scheduling problem (OPBSP) consists, therefore, in
finding such an optimal schedule. On the one hand, we introduce the dynamical
optimization approach to mine scheduling in the deterministic case, and we
propose a class of (suboptimal) adaptive strategies, the so-called index
strategies. We show that they provide upper and lower bounds for the NPV, and
we provide numerical results. On the other hand, we introduce a theoretical
framework for OPBSP under uncertainty and learning.
