  Valuing mineral assets is a challenging task that is highly dependent on the
supply (geological) uncertainty surrounding resources and reserves, and the
uncertainty of demand (commodity prices). In this work, a graph-based
reasoning, modeling and solution approach is proposed to jointly address
mineral asset valuation and mine plan scheduling and optimization under supply
and demand uncertainty in the "mining complex" framework. Three graph-based
solutions are proposed: (i) a neural branching policy that learns a
block-sampling ore body representation, (ii) a guiding policy that learns to
explore a heuristic selection tree, (iii) a hyper-heuristic that manages the
value/supply chain optimization and dynamics modeled as a graph structure.
Results on two large-scale industrial mining complexes show a reduction of up
to three orders of magnitude in primal suboptimality, execution time, and
number of iterations, and an increase of up to 40% in the mineral asset value.

Artificial Intelligence;Machine Learning