Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 7 Dec 2022.
Builds on
8 of 19 references.
References · 8
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
- 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 318
- 2021Combinatorial optimization and reasoning with graph neural networkscited by 268
- 2020Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Researchcited by 78
- 2020Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregationcited by 4
- 2021Structured Convolutional Kernel Networks for Airline Crew Schedulingcited by 2
- 2022Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexescited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.