发表机构
Adelaide University（阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出露天矿开采作业规划的机会约束双目标进化优化方法，在矿石品位不确定下最大化产量并最小化车队成本，实验表明NSGA-II和NSGA-III性能最佳，并强调不确定性大小与结构的重要性。
AI 中文摘要
露天矿开采作业规划涉及在满足生产、设备和矿石质量要求的同时分配有限资源。现有方法通常假设矿石品位是确定性的，或依赖于不确定性下的基于模拟的评估。在本文中，我们提出了在矿石品位不确定下露天矿开采作业规划问题的机会约束双目标公式。我们旨在最大化矿石产量并最小化车队成本，同时通过机会约束满足随机质量要求。我们将矿石品位建模为独立的服从正态分布的随机变量，并推导出双侧机会约束的确定性重构，从而在适应度评估中避免采样或模拟。我们在不同不确定性和结构水平下的基准实例上评估了四种多目标进化算法。我们的结果表明，NSGA-II和NSGA-III通常获得最佳的可行矿石产量值，其中NSGA-II所需计算时间更少。GSEMO的计算成本最低，但获得可行解的一致性较差，且通常提供较低的解决方案质量。我们还观察到，较高的置信度和不确定性水平会降低可行性，其效果取决于不确定性结构。结果强调了在短期露天矿作业规划中同时考虑不确定性的大小和结构的重要性。
英文摘要
Open-pit mining operational planning involves allocating limited resources while satisfying production, equipment, and ore-quality requirements. Existing approaches often assume deterministic ore grades or rely on simulation-based evaluation under uncertainty. In this paper, we propose a chance-constrained bi-objective formulation of the open-pit mining operational planning problem under uncertain ore grades. We aim to maximize ore production and minimize fleet cost while satisfying stochastic quality requirements through chance constraints. We model ore grades as independent normally distributed random variables and derive a deterministic reformulation of the two-sided chance constraints, avoiding sampling or simulation during fitness evaluation. We evaluate four multi-objective evolutionary algorithms on benchmark instances under different levels and structures of uncertainty. Our results show that NSGA-II and NSGA-III generally obtain the best feasible ore-production values, with NSGA-II requiring less computational time. GSEMO has the lowest computational cost but obtains feasible solutions less consistently and generally provides lower solution quality. We also observe that higher confidence and uncertainty levels reduce feasibility, with the effect depending on the uncertainty structure. The results highlight the importance of considering both the magnitude and structure of uncertainty in short-term open-pit operational planning.
