Multiple criteria decision making (MCDM) is a supporting tool which is widely spread in different areas of science and industry. Many researchers have confirmed that MCDM methods can be useful for selecting the best solution in many different problems. In this paper, two novel methods are presented and applied on existing decision-making processes in the mining industry. The first method is multiple criteria ranking by alternative trace (MCRAT) and the second is ranking alternatives by perimeter similarity (RAPS). These two novel methods are demonstrated in decision-making problems and compared with the ranking of the same alternatives by other MCDM methods. The mining process often includes drilling and blasting operations as the most common activities for exploitation of raw materials. For optimal blasting design it is important to select the most suitable parameters for the blasting pattern and respect characteristics of the working environment and production conditions. By applying novel methods, how to successfully select the most proper blasting pattern respecting all conditions that must be satisfied for economic aspects and the safety of employees and the environment is presented.

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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.

The explosion caused by detonation of explosive materials is followed by release of a large amount of energy. Whereby, a greater part of energy is used for rock destruction, and part of energy, in the form of seismic wave, is lost in the rock mass causing rock mass oscillation. Investigations of the character and behavior of the pattern of seismic wave indicate that the intensity and nature of the seismic wave are influenced by rock mass properties, and by blasting conditions. For evaluation and control of the seismic effect of blasting operations, the most commonly used equation is that of M.A. Sadovskii. Sadovskii's equation defines the alteration in the velocity of rock mass oscillation depending on the distance, the quantity of explosives, blasting conditions and geological characteristics of the rock mass, and it is determined based on trial blasting for a specific work environment. Thus, this paper offers analysis of the method for determination of parameters of the rock mass oscillation equation, which are conditioned by rock mass properties and blasting conditions. Practical part of this paper includes the experimental research carried out at Majdanpek open pit, located in the northern part of eastern Serbia and the investigations carried out during mass blasting at Nepričava open pit, located in central Serbia. In this paper, parameters n and K from Sadovskii's equation were determined in three ways-models in the given work environment. It was noted that, in practice, all three models can be successfully used to calculate the oscillation velocity of the rock masses.

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.