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A matheuristic approach for optimizing mineral value chains under uncertainty
Optimization and Engineering ( IF 2.1 ) Pub Date : 2021-04-27 , DOI: 10.1007/s11081-021-09629-9
Amina Lamghari , Roussos Dimitrakopoulos , Renaud Senécal

Mineral value chains, also known as mining complexes, involve mining, processing, stockpiling, waste management and transportation activities. Their optimization is typically partitioned into separate stages, considered sequentially. An integrated stochastic optimization of these stages has been shown to increase the net present value of the related mining projects and operations, reduce risk in meeting production targets, and lead to more robust and coordinated schedules. However, it entails solving a larger and more complex stochastic optimization problem than separately optimizing individual components of a mineral value chain does. To tackle this complex optimization problem, a new matheuristic that integrates components from exact algorithms (relaxation and decomposition), machine learning techniques (reinforcement learning and artificial neural networks), and heuristics (local improvement and randomized search) is proposed. A general mathematical formulation that serves as the basis for the proposed methodology is also introduced, and results of computational experiments are presented.



中文翻译:

在不确定性条件下优化矿产价值链的数学方法

矿产价值链,也称为采矿综合体,涉及采矿,加工,储存,废物管理和运输活动。他们的优化通常分为几个阶段,依次考虑。这些阶段的综合随机优化已显示出可以增加相关采矿项目和运营的净现值,降低达到生产目标的风险,并导致更稳健和协调的进度。但是,与分别优化矿产价值链的各个组成部分相比,它需要解决更大,更复杂的随机优化问题。为了解决这一复杂的优化问题,我们采用了一种新的数学方法,该方法将精确算法(松弛和分解)中的组件集成在一起,提出了机器学习技术(强化学习和人工神经网络)和启发式方法(局部改进和随机搜索)。还介绍了可作为所提出方法基础的通用数学公式,并给出了计算实验的结果。

更新日期:2021-04-27
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