Open-pit mining is widely used to extract natural resources. Low cut-off grades and large operations can make open-pit mining profitable. An important challenge is to determine the optimum production schedule. Usually the goal is to maximize the net present value of the project while delivering ore to the plant at full capacity. The best production plan would require complete knowledge of the orebody and all other engineering and economic parameters. An estimated block model is often used to determine the production schedule. Uncertainty is inevitable with widely spaced drill holes. The open-pit production schedule based on estimated models may be suboptimal and affected dramatically by grade uncertainty. The research documented herein develops, implements and verifies four mixed integer optimization frameworks for long-term production scheduling in the presence of grade uncertainty. The main contributions of this research are (1) consideration of cost of grade uncertainty to influence the production plan, (2) accounting for the linear and nonlinear effects of the grade uncertainty on the long-term mine planning, (3) development of a mixed integer linear programming model that maximizes NPV and minimizes the cost of the grade uncertainty by considering a stockpile, and finally (4) implementation of a quadratic optimization model accounts for grade uncertainty in the long-term production plan.

This book presents a collection of papers on topics in the field of strategic mine planning, including orebody modeling, mine-planning optimization and the optimization of mining complexes. Elaborating on the state of the art in the field, it describes the latest technologies and related research as well as the applications of a range of related technologies in diverse industrial contexts.

The mining business faces continual risks in producing metals and raw materials under fluctuating market demand. At the same time, the greatest uncertainty driving the risk and profitability of mining investments is the geological variability of mineral deposits. This supply uncertainty affects the prediction of economic value from the initial valuation of a mining project through mine planning, design and production scheduling. This book is the first of its kind, presenting state-of-the-art stochastic simulation and optimization techniques and step-by-step case studies. Quantification of geological uncertainty through new efficient conditional simulation techniques for large deposits, integration of uncertainty to stochastic optimization formulations for design and production scheduling and the concurrent management of risk are shown to create flexibility, options and oportunities, increase asset value, cashflows and return on investment. New approaches introduced include resource/reserve risk quantification, cost-effective drilling programs, pit design and long-term production scheduling optimization with simulated orebodies, ore reserve classification, geologic risk discounting, waste managing and demand driven scheduling, risk assessment in meeting project production schedules ahead of mining, risk based optimal stope design, options valuation when mining. Applications include commodities such as gold, copper, nickel, iron ore, coal and diamonds.

"Mining operations typically face critical operational decisions based on uncertain information. This uncertainty is inherent to the mining process and cannot be overcome, as materials have to be extracted from underground, which can never be fully explored. This thesis addresses two pertinent aspects of many mining operations while directly incorporating this inherent uncertainty into the optimization. The uncertainty is used to our advantage by minimizing the resulting downside risk while maximizing the upside potential. The first aspect, addressed in Chapter 2, is the optimization of infill drilling schemes, a decision faced by every single mining operation on various scales. The second aspect, addressed in Chapter 3, focuses on the stochastic optimization of a production schedule of a block caving operation, a popular mass mining alternative to open pit mines. The optimization of infill drilling is based on a multi-armed bandit (MAB) framework. The novelty of this approach is the application of an advanced machine learning method like MAB to find an elegant solution to the infill drilling problem. The method is applied to a case study of a gold mining complex. The main issue in this mining complex is the uncertainty in their long-term, multi-element stockpiles on which the infill drilling optimization is performed. Stockpiles are often very variable, but their uncertainty can be quantified using conventional stochastic orebody modelling. Although the infill drilling optimization is applied to a stockpile, the proposed method is more general and can be applied to any deposit. The approach proposed in Chapter 3 is the first stochastic optimization approach for scheduling a block cave mine that explicitly includes grade and hang-up uncertainty into the optimization. Hang-up uncertainty is defined as the uncertainty related to ore that clogs the draw points due to aberrant fracturing. The grade uncertainty is incorporated via stochastic orebody simulations, similar to what has been employed for open pit operations in the past, while hang-up uncertainty, the main contribution of this work, is incorporated by tracking and minimizing the delays caused by hang-ups over multiple scenarios. The results of the case study show a reduction of the annual overtime incurred due to delays of 95 % when the hang-up uncertainty is included in the optimization compared to when hang-ups are ignored." --

"Mine production scheduling consists of defining the extraction sequence and process allocation of mineralized material over some length of time. These decisions can be made at different time steps, which will entail varying objectives subject to different technical and operational constraints. Long-term mine production scheduling usually takes place at an annual scale for the entire life of mine and aims to maximize the net present value of the project while satisfying the mining and processing capacities. Short-term mine production scheduling consists of developing an extraction sequence on a shorter time scale, either months, weeks, or days. The goal is typically to maximize compliance with the production targets imposed by the long-term plan while considering more detailed operational constraints. Historically, these optimization frameworks have relied on the assumption of perfect knowledge of highly uncertain inputs. Developments in the field of stochastic mine planning have shown that incorporating uncertainty into the optimization of mine production schedules can add significant economic value while also minimizing the risk of deviating from production targets. This thesis will explore the benefits that stochastic mine planning can offer when applied to both long and short-term production scheduling problems.For the first exercise, the long-term mine production schedule of a rare earth element (REE) project is generated under geological uncertainty using a stochastic optimization framework. The uncertainty in REE grades is modelled using an efficient joint-simulation technique to preserve the strong cross-element relationships. The proposed approach avoids the use of the conventional total rare earth oxide grade. The stochastic long-term schedule is benchmarked against a deterministic schedule generated using an industry standard optimizer. The stochastic solution generates a 20% increase in expected NPV, ensures better utilization of the processing plant, and delivers a superior ore feed in terms of satisfying mineral and REE blending targets.For the second exercise, a formulation is proposed that simultaneously optimizes the short-term equipment plan and production schedule under both geological and equipment performance uncertainty. The proposed approach rectifies certain limitations of previous work in stochastic short-term planning by: incorporating a location-dependant shovel movement optimization; generating more realistic equipment performance scenarios; developing a new approach to facilitate more practical mine designs; and proposing model improvements to allow for a more efficient optimization of very large problem instances. The model is applied to a large copper mining complex and is compared to a more traditional approach, where the same formulation is implemented using averaged inputs for geology and equipment performance. The stochastic solution is more effective in mitigating the risk of deviating from tonnage targets at each processing destination, and the integration of equipment performance variability allows the stochastic optimizer to generate a block extraction sequence that is far more likely to be achieved." --

The conferences on ‘Applications for Computers and Operations Research in the Minerals Industry’ (APCOM) initially focused on the optimization of geostatistics and resource estimation. Several standard methods used in these fields were presented in the early days of APCOM. While geostatistics remains an important part, information technology has emerged, and nowadays APCOM not only focuses on geostatistics and resource estimation, but has broadened its horizon to Information and Communication Technology (ICT) in the mineral industry. Mining Goes Digital is a collection of 90 high quality, peer reviewed papers covering recent ICT-related developments in: - Geostatistics and Resource Estimation - Mine Planning - Scheduling and Dispatch - Mine Safety and Mine Operation - Internet of Things, Robotics - Emerging Technologies - Synergies from other industries - General aspects of Digital Transformation in Mining Mining Goes Digital will be of interest to professionals and academics involved or interested in the above-mentioned areas.

This conference proceedings presents the research papers in the field of mine planning and mining equipment including themes such as mine automation, rock mechanics, drilling, blasting, tunnelling and excavation engineering. The papers presents the recent advancement and the application of a range of technologies in the field of mining industry. It is of interest to the professionals who practice in mineral industry including but not limited to engineers, consultants, managers, academics, scientist, and government staff.

This edited volume includes all papers presented at the 22nd International Conference on Mine Planning and Equipment Selection (MPES), Dresden, Germany, 2013. Mineral Resources are needed for almost all processes of modern life, whilst the mining industry is facing strict requirements regarding efficiency and sustainability. The research papers in this volume deal with the latest developments and research results in the fields of mining, machinery, automatization and environment protection.