Dynamic decision-making can significantly increase the value of mining projects and deliver better sustainability outcomes under conditions of uncertainty. Despite its benefits, dynamic decision-making is rarely used by the mining industry, possibly because of the complexity of the algorithms used to make sequential decisions under uncertainty and difficulties in visualising the optimal strategies. To achieve better outcomes in practice, decision-makers in industry would benefit from a tool that would help them to understand and implement the results of the analysis.
CSIRO is developing innovative dynamic decision support tools that include an intuitive graphical display of the boundaries between the regions of different optimal decisions, which would significantly assist optimal sequential decision-making under uncertainty. The dynamic decision support tool is based on novel stochastic optimal control methodologies that can be used to optimise decisions in situations with multiple uncertain variables and decisions (Tarnopolskaya, Chen and Bao, 2015; Chen et al, 2015; Langrené et al, 2015). The types of market uncertainties that can be considered include commodity prices, interest rates and exchange rate, while the key orebody uncertainties include the reserves, boundaries between ore types and even geometallurgical parameters.
The dynamic decision support tools have a number of benefits and can be used to:
- construct optimal operational strategies and to optimally manage resources projects
- gain insight into optimal strategies under different market conditions and project settings compare expected project values under different project settings and to select the setting that provides best sustainability outcomes.
This extended abstract demonstrates the use and benefits of the dynamic decision support tools using a synthetic case study of a gold mine.
Chen, W, Langrené, N, Tarnopolskaya, T, Zhu, Z and Cooksey, M, 2016. Dynamic decisions under uncertainty – a case study on a gold mine, in Proceedings Life-of-Mine 2016 Conference, pp 20–23 (The Australasian Institute of Mining and Metallurgy: Melbourne).