用AI生成地质不确定性场景，实现矿山长期规划的高效优化。
Deep Learning Decision Support System for Open-Pit Mining Optimisation: GPU-Accelerated Planning Under Geological Uncertainty
- 通过变分自编码器生成5万组地质样本，模拟多场景矿体分布。
- 结合遗传算法与强化学习，实现65,536种场景并行评估，提速百万倍。
- 适合矿业决策者和智能规划研究者，提升复杂条件下的投资回报率。
本研究是人工智能增强型决策支持系统（DSS）的第二部分，扩展了Rahimi（2025，第一部分）的工作，提出一个完全具备不确定性感知能力的长期露天矿开采优化框架。地质不确定性通过在50,000个空间品位样本上训练的变分自编码器（VAE）建模，可生成保持地质连续性和空间相关性的概率性、多情景矿体实现。这些情景通过混合元启发式引擎进行优化，该引擎融合了遗传算法（GA）、大邻域搜索（LNS）、模拟退火（SA）以及基于强化学习的自适应控制。采用ε-约束松弛策略管理种群探索阶段，可在搜索早期发现近可行调度，并逐步收紧至严格满足约束。借助GPU并行评估，可同时分析65,536个地质情景，实现近实时可行性分析。结果表明，相比IBM CPLEX，运行时间最高提升120万倍，在地质不确定性下预期净现值（NPV）显著更高，证实该DSS是一个可扩展且抗不确定性的智能矿山规划平台。
原文摘要 · Abstract (English)
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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