An underground mine is a very complex production system within the mining industry. Building up the underground mine development system is closely related to the installation of support needed to provide the stability of mine openings. The selection of the type of support system is recognized as a very hard problem and multi-criteria decision making can be a very useful tool to solve it. In this paper we developed a methodology that helps mining engineers to select the appropriate support system with respect to geological conditions and technological requirements. Accordingly, we present a novel hybrid model that integrates the two following decision-making components. First, this study suggests a new approach for calculating the weights of criteria in an objective way named the Modified Preference Selection Index (MPSI) method. Second, the Magnitude of the Area for the Ranking of Alternatives (MARA) method is proposed as a novel multi-criteria decision-making technique for establishing the final rank of alternatives. The model is tested on a hypothetical example. Comparative analysis confirms that the new proposed MPSI–MARA model is a very useful and effective tool for solving different MCDM problems.

The paper proposes a problem-solving approach in the area of underground mining, related to the evaluation and selection of the optimal mining method, employing fuzzy multiple-criteria optimization. The application of fuzzy logic to decision-making in multiple-criteria optimization is particularly useful in cases where not enough information is available about a given system, and where expert knowledge and experience are an important aspect. With a straightforward objective, multiple-criteria decision-making is used to rank various mining methods relative to a set of criteria and to select the optimal solution. The considered mining methods represent possible alternatives. In addition, various criteria and subcriteria that influence the selection of the best available solution are defined and analyzed. The final decision concerning the selection of the optimal mining method is made based on mathematical optimization calculations. The paper demonstrates the proposed approach as applied in a case study.

Production planning in an underground mine plays a key activity in the mining company business. It is supported by the fact that mineral industry is unique and volatile environment. There are two uncertain parameters that cannot be managed by planners, metal price, and operating costs. Having ability to quantify and incorporate them in the process of planning can help companies to do their business in much easier way. We quantify these uncertainties by the simulation of mean reverting process and Itô‐Doob stochastic differential equation, respectively. Mineral deposit is represented as a set of mineable blocks and room and pillar mining method is selected as a way of mining. Multicriteria clustering algorithm is used to create areas inside of mineral deposit that have technological characteristics required by the planners. We also developed a way to forecast the volatility of economic values of these areas through the planning period. Fuzzy 0‐1 linear programming model is used to define the sequence of mining of these areas by maximization of the expected value of the fuzzy future cash flow. Model was tested on small hypothetical lead‐zinc mineral deposit and results showed that our approach was able to solve such complex problem.

Large capital intensive projects, such as those in the mineral resource industry, are often associated with diverse sources of both endogenous and exogenous risks and uncertainties. These risks can greatly influence the project profitability. Having the ability to plan for these uncertainties is increasingly recognized as critical to long-term mining project success. In the mining industry in particular, the relationships between input variables that are controllable, and those that are not, and the physical and economic outcomes are complex and often nonlinear. The value of managerial flexibility is assessed using data on prices, costs, discount rates, grades, ore extraction, and metal output. Monte Carlo simulation of the mean reversion process is used to forecast revenue data based on an initial metal price, by using annualized volatility. Monte Carlo simulation of the Geometric Brownian Motion is used to forecast operating costs. To quantify the uncertainty in the parameters within a project such as capital investment, ore grade, and mill recovery, we used triangular, uniform, and normal statistical distribution, respectively. To decrease uncertainty related to selection of the appropriate discount rate, we have applied the concept of fuzzy sets theory. The result is a Net Present Value (NPV) based on the cash flows generated by the simulation over the timeframe of the project. When using fuzzy numbers, the fuzzy NPV itself is the payoff distribution from the project. The model explains investment behavior satisfactorily, both from a statistical and from an economic point of view.

Knowing mechanical properties of rocks incorporated with ultrasonic waves velocities investigations provide a secure basis for mine modelling. Uniaxial compressive strength, dynamic elastic modulus and ultrasonic waves velocities are just some of the rock properties that indicate on presence of discontinuities, stratification and fissures of rocks. Determining the relationship of these rock properties testing the rock samples that are corresponding to different rock types extracted from different mineral deposits represents the core of this study. In this paper, we statistically analyzed measured and calculated rock properties. We established the connection between these parameters creating empirical correlation equations. Using least squares method and simple linear regression analysis, we defined correlation coefficients and also developed empirical correlation equations.

In order to evaluate and control the seismic effect of blasting, as well as its planning, it is required to determine the soil oscillation law, with the strike/mining facilities to be protected. One of the most commonly used equations is that of M.A. Sadovskii, defining the law of alteration in the oscillation velocity of the soil depending on distance, the explosive amount, and conditions of blasting and geologic characteristics of the soil; all of this being determined on the basis of test blasting for the specific work environment. In the Sadovskii equation two parameters, K and n appear and they are conditioned both by rock mass characteristics and blasting conditions. The practical part of this study includes experimental investigations performed in the Veliki Krivelj Open Pit in the Bor District located in Eastern Serbia and investigations carried out during mass mining in the Kovilovača Open Pit near Despotovac, Eastern Serbia. Thus this paper offers several modes for determination of parameters K and n in the Sadovskii equation. To determine the parameters in the Sadovskii formula, in addition to the usual least square method, two more new models were applied. In the models the parameters K and n were determined by applying the quotient of the relative growth of oscillation velocities and reduced distances for Model 2. The link between the parameters K and n is determined by applying the trapezoidal formula for finding the value of definite integral for Model 3. In doing so, it was noted that all three models can be used to calculate the oscillation velocity of the rock mass.