Deep Learning Decision Support System for Open-Pit Mining Optimisation: GPU-Accelerated Planning Under Geological Uncertainty

• URL: http://arxiv.org/abs/2511.18296v1
• Date: Sun, 23 Nov 2025 05:27:04 GMT
• Title: Deep Learning Decision Support System for Open-Pit Mining Optimisation: GPU-Accelerated Planning Under Geological Uncertainty
• Abstract summary: This study introduces a fully uncertainty-aware optimization framework for long-term open-pit mine planning.<n> Geological uncertainty is modelled using a Variational Autoencoder trained on 50,000 spatial grade samples.<n>Results demonstrate up to 1.2 million-fold improvement over IBM CPLEX and significantly higher expected NPV under geological uncertainty.
• Score: 0.7310043452300737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents Part II of an AI-enhanced Decision Support System (DSS), extending Rahimi (2025, Part I) by introducing a fully uncertainty-aware optimization framework for long-term open-pit mine planning. Geological uncertainty is modelled using a Variational Autoencoder (VAE) trained on 50,000 spatial grade samples, enabling the generation of probabilistic, multi-scenario orebody realizations that preserve geological continuity and spatial correlation. These scenarios are optimized through a hybrid metaheuristic engine integrating Genetic Algorithms (GA), Large Neighborhood Search (LNS), Simulated Annealing (SA), and reinforcement-learning-based adaptive control. An ε-constraint relaxation strategy governs the population exploration phase, allowing near-feasible schedule discovery early in the search and gradual tightening toward strict constraint satisfaction. GPU-parallel evaluation enables the simultaneous assessment of 65,536 geological scenarios, achieving near-real-time feasibility analysis. Results demonstrate up to 1.2 million-fold runtime improvement over IBM CPLEX and significantly higher expected NPV under geological uncertainty, confirming the DSS as a scalable and uncertainty-resilient platform for intelligent mine planning.

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