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In the mining environment, mine planning is complicated by the presence of unfavorable environmental conditions, limited knowledge of the shape and size of the deposit, ore body characteristics, and volatile market conditions. In this paper, we propose a top-down algorithmic approach to strategically optimize the cutoff grade and net present value (NPV), and implement its solutions at the operation level, while simultaneously mitigating operation risks, to maximize the life of an ultra-deep gold mine from the Witwatersrand Basin, South Africa. To date, the Witwatersrand Basin has contributed about 28% of the world's total gold supply from a series of Mesoarchaean quartz pebble conglomerate units (referred to as reefs). Through a quantitative analysis using algebraic and stochastic methods, we ranked mining variables in terms of their margin sensitivity and impact/adjustability efficacy. The results of this study showed the following. By using our proposed approach, an underground mine plan can be optimized by focusing on few key variables. Strategic mining of combinations of high-grade panels with low-grade panels and counter-balancing their risk profiles can yield optimal executable mine plan results (i.e., higher NPV, ideal profit margin, and lower risk) without sterilizing a given mineral resource for underground mining operations. [ABSTRACT FROM AUTHOR]

This paper discusses a new methodology to define and measure utilisation of mine planning software. An initial data set compiled in 2012 in an online database was updated in 2014 with additional and new information for software utilised in the South African mining industry. However, the online database only serves as a data repository and presentation platform. A methodology was required to analyse the data in the database to infer extent of utilisation of the software to enable better decision-making strategies by various stakeholders that include mining companies, software providers, consulting companies and educational institutions. The new methodology uses three variables, namely commodity, functionality and time stamp. In this paper, only one of the six mine planning software functionalities called the ‘Optimisation’ functionality is applied on five selected commodities, using the 2012 and 2014 time stamps. The work presented in this paper is part of a PhD research study in the School of Mining Engineering at the University of the Witwatersrand. [ABSTRACT FROM AUTHOR]

Some mining operations eventually transition underground because surface mining becomes increasingly expensive as one progresses downward. Mining firms often delay this transition because large underground infrastructure costs are incurred up front, whereas underground extraction may occur over decades. When and how deep to install the underground infrastructure, as well as extraction schedules above and below ground, are decisions with a sizable impact on profits. This article addresses these questions while considering realistic factors, including choices of cutoff grades (minimum ore concentration at which the extracted material is processed to recover ore) and mining rates. We present a large longest-path representation of the problem and show that it can be solved via a series of small longest-path problems. The latter representation is not a decomposition of the original network but takes advantage of the structure of the problem. Together, the small networks require only a few seconds to solve. We illustrate our approach using data from a South African mine and provide insights regarding the effects of ore prices, discount rates, and their interactions on the characteristics of optimal solutions; we find that common wisdom is not always applicable. Our solutions have significantly higher profits than benchmark solutions, representing up to billions of dollars. [ABSTRACT FROM AUTHOR]