@inproceedings{1710_40_Maybee, author={Maybee, B and Yana, J}, editor={Hudyma, M and Potvin, Y}, title={Using the single index model to create a short-term mine plan}, booktitle={UMT 2017: Proceedings of the First International Conference on Underground Mining Technology}, date={2017}, publisher={Australian Centre for Geomechanics}, location={Perth}, pages={499-509}, abstract={How to effectively plan, sequence and schedule for extraction the areas within an underground mining operation is a financial reward-maximising issue for the mining community. The mining industry is one that has a heightened level of uncertainty due to the fact that the product is not known with certainty until after it has been produced. As a result, decisions on how to allocate the limited resources that are available in both the long-term and short-term must be made with less than perfect information. If the underground mine is divided into distinct areas, and each area is scheduled independently as a discrete, standalone area, the characteristics of the individual areas can be likened to those of a financial asset, having an expected return over a period of time, as well as risk associated with that return based on the uncertainty surrounding the underlying factors that contribute to its value. There are limited theoretical tools that allow mine planning professionals to confidently plan their extraction sequences in a manner that minimises the risk of stochastic changes in industry forces. As a result, this study proposes the use of the single index model (SIM) as part of the mine scheduling process, providing a tool to assist decision-makers in selecting optimum mine areas for inclusion in the short-term plan so that proper mining sequences can be established with the most effective allocation of resources. This process is similar to that of an investor considering a number of alternative assets for investment, and using the SIM to identify the assets to be included in their portfolio. }, keywords={mine planning}, keywords={single index model}, keywords={portfolio optimisation}, keywords={decision support}, doi={10.36487/ACG_rep/1710_40_Maybee}, url={https://papers.acg.uwa.edu.au/p/1710_40_Maybee/} }