This paper presents an improved appraisal process that correctly recognises the increased expected value of mining operations attributable to intelligent management response to changing operating conditions and market prices. It recognises the option value of flexibility in the exploitation of deposits, and thus leads to better selection of properties for finance and development. This evaluation method is based on a computationally efficient procedure for examining the opportunities provided by possible price changes that may occur over the project lifetime. It implicitly looks at all possible future price scenarios using a limited range of typical price profiles, therefore covering the range of possibilities without exploring it exhaustively. It contrasts with conventional analyses that assume that prices do not fluctuate through the lifetime of the project. The procedure was developed in collaboration with experienced mining professionals and actual cases in mining and other extractive industries.

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Experience and intuition have traditionally been central to decision-making in mining because of the frequent lack of quantitative data. Qualitative analysis is based primarily on the judgement, knowledge and experience of one or more experts. In cases where limited information is available, then subjective probabilities, based on general professional experience, knowledge, and opinion of experts, can be the basis for analysis. A methodology for qualitative decision-making using the analytic hierarchy process (AHP) mathematics and sensitivity analyses is presented herein. This paper presents a series of case studies in different mining scenarios to demonstrate the application of AHP. These relate to: investment analysis of new technology; ground support design; tunnelling systems' design; shaft location selection; and mine-planning risk assessment. A review is given of the AHP methodology for qualitative decision making based on field applications. V. N. Kazakidis (

Mass mining methods provide alternatives in developing deeper and lower-grade mineral deposits. Consequently, block cave mining has been increasingly popular mass mining method, especially for large copper deposits currently being mined by open pit methods. This study adopts similar concepts as in stochastic open pit production planning to the planning of block cave mines, to evaluate their effectiveness in a different approach to mass mining. The main contribution of this study is the incorporation of the uncertainty of delays from hang-ups and grades directly into the production scheduling process of a cave mining operation. Hang-up uncertainty relates to the uncertainty linked to the occurrence of ore that clogs the production draw points. This clogging causes time delays in the production cycle leading to tonnage losses and additional costs. Grade uncertainty is incorporated by means of stochastic orebody simulations. Both uncertainty sources are directly linked to the extraction decisions and influence the optimized schedules. The proposed stochastic integer programming model is applied to the optimization of the long-term schedule of a large-scale, low-grade copper deposit by taking into account hang-up delays in block caving. The results of the optimization maximizing net present value clearly show the capability of the formulation to mitigate the effects of both grade and hang-up uncertainty.

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