arXiv：cs.AI· Ishara Hewa Pathiranage, Aneta Neumann·· 6 小时前AI 评分27
进化优化如何求解动态机会约束露天矿调度问题
On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem
AI 导读
研究针对块经济价值随机、采矿与加工能力随时间变化的动态机会约束露天矿调度问题，提出双目标进化公式，同时最大化期望折现利润并最小化其标准差。为应对动态变化，作者设计基于多样性的变化响应机制，在检测到变化时修复部分不可行解并引入额外可行解，并在四种多目标进化算法与基于重评估的基线策略上对比。六个矿山实例实验显示，该方法在不同不确定性水平和变化频率下均持续优于基线。
正文
Abstract:Open-pit mine scheduling is a complex real-world optimization problem that involves uncertain economic values and dynamically changing resource capacities. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to uncertain and changing environments. However, uncertainty and dynamic changes are often studied in isolation in real-world problems. In this paper, we study a dynamic chance-constrained open-pit mine scheduling problem in which block economic values are stochastic and mining and processing capacities vary over time. We adopt a bi-objective evolutionary formulation that simultaneously maximizes expected discounted profit and minimizes its standard deviation. To address dynamic changes, we propose a diversity-based change response mechanism that repairs a subset of infeasible solutions and introduces additional feasible solutions whenever a change is detected. We evaluate the effectiveness of this mechanism across four multi-objective evolutionary algorithms and compare it with a baseline re-evaluation-based change-response strategy. Experimental results on six mining instances demonstrate that the proposed approach consistently outperforms the baseline methods across different uncertainty levels and change frequencies.
| Comments: | Accepted to publish in 2026 IEEE World Congress on Computational Intelligence (WCCI), This version corrected typo in Table 2 |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.13385 [cs.NE] |
| (or arXiv:2604.13385v3 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2604.13385 arXiv-issued DOI via DataCite |
Submission history
From: Ishara Hewa Pathiranage Mrs [view email]
[v1]
Wed, 15 Apr 2026 01:16:01 UTC (259 KB)
[v2]
Sat, 3 Oct 2026 02:49:47 UTC (259 KB)
[v3]
Tue, 6 Oct 2026 04:33:11 UTC (259 KB)
来源：arXiv：cs.AI · arxiv.org