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A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming

机译：使用随机整数规划的铁矿石生产调度的新风险优化方法

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Stochastic integer programming (SIP) has recently been studied to manage the risk caused by geological uncertainty when solving mine planning and production scheduling problems of open pit mines. However, similar to other mathematical programming techniques that deploy integer variables, the main obstacle of applying SIP on real-life datasets stems from the enormous number of integer variables required by its mathematical formulation, which is a function of number of mining blocks being processed and lifespan of the mining project. In this paper, a new framework is proposed for stochastic mine planning process which makes the application of SIP on large-scale datasets tractable. Firstly, mining blocks of simulated orebody models are clustered using TopCone algorithm to significantly reduce the scale of the data. A new SIP model is then developed to work on aggregated blocks so not only the net present value (NPV) is maximised and the risk of not meeting production targets is minimised, but also solution can be obtained in a practical timeframe. The scheduling result of the new SIP model is also compared to an integer programming (IP) model to highlight the ability to manage risk and generating higher NPV on a case study of a large-scale multi-element iron ore deposit in Pilbara region, Western Australia.
机译：最近研究了随机整数规划（SIP），以管理在解决露天矿山的矿山规划和生产调度问题时，管理地质不确定性引起的风险。然而，类似于部署整数变量的其他数学编程技术，将SIP应用于现实生活数据集的主要障碍来自其数学制定所需的巨大数量的整数变量，这是正在处理的挖掘块数的函数矿业项目的寿命。本文提出了一种新框架，用于随机矿山规划过程，使SIP在大规模数据集的应用中的应用。首先，使用Topcone算法群集模拟矿体模型的挖掘块，以显着降低数据的比例。然后开发出新的SIP模型以在聚合块上工作，因此不仅最大限度地净值（NPV）而且不达到生产目标的风险最小化，而且还可以在实际时间范围内获得解决方案。新SIP模型的调度结果也与整数编程（IP）模型进行了相比，以突出显示风险的能力，并在西部Pilbara地区的大型多元素铁矿矿床的情况下进行风险和更高的NPV澳大利亚。

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