Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty

TL;DR

This paper introduces a graph-based framework for mineral asset valuation and mine planning that effectively manages supply and demand uncertainties, significantly improving accuracy and computational efficiency.

Contribution

It presents three novel graph-based solutions for joint valuation and scheduling under uncertainty, advancing mining optimization techniques.

Findings

Up to 1000x reduction in suboptimality and execution time.

Up to 40% increase in mineral asset value.

Effective large-scale industrial application demonstrated.

Abstract

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.