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

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 {\epsilon}-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.