Mining method selection by using an integrated model

International Research Journal of Applied and Basic Sciences

© 2013 Available online at www.irjabs.com

ISSN 2251-838X / Vol, 6 (2):199-214

Science Explorer Publications

Mining method selection by using an integrated

model

Shahram Shariati1,Abdolreza Yazdani-Chamzini2, Behrang Pourghaffari Bashari3

1. Department of geology, Sari branch, Islamic Azad University, Sari, Iran.

2. Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran, Iran.

3. Mining Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Corresponding Author email:behrangpourghaffari@gmail.com, shariati.shahram@gmail.com and

shariati@ayerma.ir, abdalrezaych@gmail.com and yazdani@ayerma.ir

ABSTRACT: The problem of mining method selection is one of the most important decisions that

should be made by mining managers and engineers. Selecting a proper underground mining method

to accomplish extraction from a mineral deposit is very significant in terms of the economics, safety

and the productivity of mining operations. The aim objective of this paper is to develop an integrated

model to selection the best mining method by using effective criteria and at the same time, taking

subjective judgments of decision makers into account. Proposed model is based on fuzzy analytic

hierarchy process (FAHP)methodology and technique for order preference by similarity to ideal

solution (TOPSIS). FAHP is appliedtodetermine the weights of the evaluation criteria for mining

method selection that these weights are inserted to the TOPSIS technique to rank the alternatives

and select the most appropriate alternative. The proposed method is applied for AngouranMine in Iran

and finally the optimum mining methods forthis mine are ranked. The study was followed by the

sensitivity analysis of the results.

Keywords:Mining method selection, MCDM, FAHP, TOPSIS

INTRODUCTION

Selection of mining method is one of the most crucial decisions in the design stage of mine that mining

engineers have to make. Selecting a mining method for mineral resources is completely dependent on the

uncertain geometrical and geological characteristic of the resource (Azadeh et al, 2010). It is necessary to the

unique characteristics of each mineral resource be taken into account in order to select the suitable mining

method for the extraction of a certain resource, so that the utilized method would have the maximum

technical-operational congruence with the geological and geometrical conditions of the mineral resource. To

make the right decision on mining method selection, all effective criteria related to the problem should be taken

into account. Increasing the number of the evaluation criteria in decision making problem makes the problem

more complex, but also the rightness of the decision increases. Therefore, there is a need for alternative

methods, which can consider all known criteria related to underground mining method selection in the decision

making process (Alpay, Yavuz, 2009). The sensitivity of this decision has led to different solutions introduced by

different researchers (Boshkov et al, 1973; Morrison, 1976; Laubscher, 1981; Nicholas, 1981; Hartman, 1982;

Intl. Res. J. Appl. Basic. Sci. Vol., 6 (2), 199-214, 2013

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Brady, Brown, 1985; Hartman, 1987; Adler, Thompson, 1897, Miller-Tait et al, 1995).

In the above-mentioned studies, mining method selection procedure has been looked from qualitative

viewpoint (Azadeh et al, 2010). Likewise two of the problems of these approaches is lack of having correct

relation between represent classes of parameters especially in near the boundary conditions and having the

same relevance of all evaluation criteria. Therefore, these studies were neither enough nor complete, as it is not

possible to design a methodology that will automatically choose a mining method for the ore body studies

(Bitarafan, Ataei, 2004).

The merit of using multi-criteria decision making (MCDM) methods is their ability to solve complex and multi

criteria problems by handling both quantitative and qualitative criteria. The MCDM methods arestrong tools for

determining the best alternative among a pool of the feasible alternatives.Technique for Order Preference by

Similarity to Ideal Solution (TOPSIS) is one common MCDM method that takes into consider the ideal and the

anti-ideal solutions simultaneously. This technique is applied by different researches because of being rational,

simple computations, and results are obtained in shorter time than other methods such as AHP (analytical

hierarchy process) and ANP (analytic network process) (Fouladgar et al, 2011; Lashgari et al, 2011).

On the other hand, AHP is widely used to calculate the weights of evaluation criteria. This method use

pair-wise comparison for obtaining the relative weights of criteria. AHP is strongly connected to human judgment

and pairwise comparisons in AHP may cause evaluator’s assessment bias which makes the comparison

judgment matrix inconsistent (Aydogan, 2011). Therefore, fuzzy analytical hierarchy process (FAHP) is

employed to solve the bias problem in AHP.

The main aim of this paper is to develop an integrated model based on FAHP and TOPSIS methods in

order to evaluate mining methods and select the best alternative in the Anguran mine.TOPSIS is employed to

select a mining method and the FAHP is applied to calculate criteria weights.

The rest of this paper is organized as follows. In section 2, a brief review of fuzzy theory is presented,

including fuzzy sets, fuzzy numbers, and linguistic variables. Section 3 illustrates the FAHP methodology for

calculating the relative weights of evaluation criteria. The procedure of the TOPSIS method is described in

section 4. The proposed model is presented in section 5. Section 6 presents an empirical study of mining

method selection. A sensitivity analysis is conducted in section 7. Finally, concluding remarks are discussed in

section 8.

Fuzzy logic

Fuzzy logic, introduced by Zadeh (1965), is a powerful tool for facing with the existing uncertainty, imprecise

knowledge, and less of information. Fuzzy numbers may be of almost any shape (though conventionally they are

required to be convex and to have finite area), but frequently they will be triangular (piecewise linear), s-shape

(piecewise quadratic) or normal (bell shaped) (Kelemenis et al. 2011).

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