论文标题
在供应和需求不确定性的矿产资产估值的图表上学习
Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty
论文作者
论文摘要
估价矿产资产是一项具有挑战性的任务，高度依赖于围绕资源和储量的供应（地质）不确定性以及需求的不确定性（商品价格）。在这项工作中，提出了一种基于图的推理，建模和解决方案方法，以共同解决矿产资产评估以及供应和需求不确定性的矿产计划的调度和优化。提出了三种基于图的解决方案：（i）一种学习块采样的矿体体表示的神经分支策略，（ii）一种指导政策，学会探索启发式选择树，（iii）一种超女性，它可以管理一个值/供应链优化和动态为图形结构。两个大规模工业矿业络合物的结果显示，原始次级次要性，执行时间和迭代次数最多减少了三个数量级，并且矿产资产价值最多增加了40％。
Valuing mineral assets is a challenging task that is highly dependent on the supply (geological) uncertainty surrounding resources and reserves, and the uncertainty of demand (commodity prices). In this work, a graph-based reasoning, modeling and solution approach is proposed to jointly address mineral asset valuation and mine plan scheduling and optimization under supply and demand uncertainty in the "mining complex" framework. Three graph-based solutions are proposed: (i) a neural branching policy that learns a block-sampling ore body representation, (ii) a guiding policy that learns to explore a heuristic selection tree, (iii) a hyper-heuristic that manages the value/supply chain optimization and dynamics modeled as a graph structure. Results on two large-scale industrial mining complexes show a reduction of up to three orders of magnitude in primal suboptimality, execution time, and number of iterations, and an increase of up to 40% in the mineral asset value.
