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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.

12 3710 14 M20 М22 Underground mining engineers and planners in our country are faced with extremely difficult working conditions and a continuous shortage of money. Production disruptions are frequent and can sometimes last more than a week. During this time, gate road support is additionally exposed to rock stress and the result is its progressive deformation and the loss of functionality of gate roads. In such an environment, it is necessary to develop a low-cost methodology to maintain a gate road support system. For this purpose, we have developed a model consisting of two main phases. The first phase is related to support deformation monitoring, while the second phase is related to data analysis. To record support deformations over a defined time horizon we use laser scanning technology together with multivariate singular spectrum analysis to conduct data processing and forecasting. Fuzzy time series is applied to classify the intensity of displacements into several independent groups (clusters).

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.

Meeting investment and operating goals with presence of different sources of uncertainties and operational constraints is critical for a successful underground mining operation and even for a mining company to survive. Small and large mining businesses are all affected by business environment. Production planning that takes into account real strength of the mining company requires from the owner or management of the company to set up acceptable and achievable investment goals (targets). In this paper we propose the production planning model that minimizes deviation from Acceptable Rate Of Return (AROR). Besides the AROR, there are operating goals success that should be also realized with minimum deviation from target values. Accordingly, the production planning can be treated as a multi-objective problem. All these objectives are integrated in multi-variable weighted Frobenius distance function that measures the deviation from established targets. Ore body is represented as a set of mineable blocks and room and pillar mining method is selected as a way of mining. We apply a multi-objective iterated greedy algorithm to define a set of blocks that should be mined every year such that deviations from target values are less than or equal to given errors of minimization. Uncertainty of metal price and operating costs are treated by mean reversion process and Geometric Brownian motion respectively. Algorithm was tested on small hypothetical lead-zinc ore body.

The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants&rsquo, production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

A shaft is a vertical passageway connecting the surface to ore deposit located deep under surface. The decision on the shaft location is a critical element in the strategic planning for underground mine development system design. Ore deposit is often composed of few independent orebodies located in different places in the space that must be interconnected into one integrated system. In this paper, we examine the case when access points to orebodies lay in the Euclidean plane. We apply fuzzy techniques to incorporate data related to ore reserve and costs into shaft location problem. Fuzzy TOPSIS method is used for the multi-criteria evaluation of the location of the base of the shaft. To identify candidate points (alternatives), we use network optimization based on applying Kruskal's algorithm and adding Steiner points. The set of candidate points is given by the union of access and Steiner points.

When deposit is composed of few ore bodies it is necessary to interconnect them into one integrated system. Suppose the deposit characteristics indicate that decline development system is preferred one. In such environment we treat development of an underground mine as access infrastructure composed of different decline sections. Access infrastructure designing can be treated as spanning the spatial network which will connect all main terminals (points). In our model we defined spatial network by adequate nonlinear constrained objective function representing the cost of mine development and ore haulage. To find the minimum value of the objective function we use Genetic algorithm.

Underground mine projects are often associated with diverse sources of uncertainties. Having the ability to plan for these uncertainties plays a key role in the process of project evaluation and is increasingly recognized as critical to mining project success. To make the best decision, based on the information available, it is necessary to develop an adequate model incorporating the uncertainty of the input parameters. The model is developed on the basis of full discounted cash flow analysis of an underground zinc mine project. The relationships between input variables and economic outcomes are complex and often nonlinear. Fuzzy-interval grey system theory is used to forecast zinc metal prices while geometric Brownian motion is used to forecast operating costs over the time frame of the project. To quantify the uncertainty in the parameters within a project, such as capital investment, ore grade, mill recovery, metal content of concentrate, and discount rate, we have applied the concept of interval numbers. The final decision related to project acceptance is based on the net present value of the cash flows generated by the simulation over the time project horizon.