Resources Policy 77 (2022) 102676
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A literature review of Multi Criteria Decision-Making (MCDM) towards
mining method selection (MMS)
Farhad Samimi Namin a, *, Aliakbar Ghadi b, Farshad Saki c
a
Department of Mining Engineering, University of Zanjan, Zanjan, Iran
Department of Materials Engineering, University of Zanjan, Zanjan, Iran
c
Mining and Metallurgical Engineering Department, Amirkabir University of Technology, Tehran, Iran
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Mining method selection
Multi criteria decision making
Fuzzy logic
Literature review
Mining Method Selection (MMS) is one of the important decisions in the mining design. The appropriate decision
guarantees economic exploitation and an unsuitable selection may lead to mining losses. The decision on mining
method depends on several parameters such as geometric factors, geotechnical, geological conditions, economic
and environmental factors, etc. The past decades, empirical models were using to select the mining method.
There are shortcomings with empirical models, such as limitations in the number of criteria and options,
decision-making in crisp and certain situations, unclear answers and dependence on experience and so on. The
using of Multi Criteria Decision Making (MCDM) has been considered to select the best mining method recently.
In this research, a comprehensive literature review are presented in order to uncover and interpret the current
research on MCDM applications in MMS. For this purpose, a reference bank has been established based on a
classification scheme which includes 55 research papers already published in 18 scholarly journals up to 2020.
Quartile Scores in the web of knowledge (Q) was used for validation of the articles. Distribution of the articles
throughout the world and according to different ore bodies was also evaluated. Results showed that AHP and
TOPSIS were the most preferred methods used in selection of the mining methods. Fuzzy decision making has
recently gained more importance for consideration of uncertainty. Also, the important role of criteria in different
ores is presented as a guide for MMS.
1. Introduction
Today, the high cost of mine developing has increased the impor­
tance of choosing a proper mining method at the designing stage. After
choosing the method, changing and replacing with another method is
very difficult and sometimes impossible. Changing the method can be so
costly that makes the project uneconomical. In the past, the choice of
mining method was based on the experience of miners or mining com­
panies. The models of Mining Method Selection (MMS) were an attempt
to record existing experiences. One of the purposes of MMS models is to
computerize the process of selecting extraction methods and doc­
umenting the experiences of engineers. Until now, many models have
been presented for selecting the method of extracting ore body. In
general, these models can be classified into three groups: qualitative
models (using flowcharts and classification tables of mining methods),
numerical scoring methods and decision-making models. The choice of
mining method was considered in 1940 and all the models presented
from that date until 1980 are categorize in the first group. Then, nu­
merical scoring methods were considered and from 2000 onwards,
decision-making models were introduced in the topics of MMS. The
evolution of the selection patterns of mining methods is shown in Fig. 1.
The models for selecting the mining methods have been divided into
qualitative and quantitative groups. Initially, to determine the appro­
priate mining method in qualitative models, the deposit characteristics
would have been compared with the conditions of application of mining
methods. Gradually, the indicators were assigned with numerical scores.
The quantitative methods have replaced qualitative methods and have
achieved different results based on the prevailing conditions in each
region and the technology available in different countries.
In the last two decades, the use of Multi Criteria Decision-Making
(MCDM) models in the process of selecting the mining method has
replaced the previous methods. Extensive research has been conducted
simultaneously with the development of decision-making models. The
purpose of using MCDM is to find the appropriate mining method that
* Corresponding author.
E-mail addresses: f.samiminamin@znu.ac.ir (F.S. Namin), ghadi@znu.ac.ir (A. Ghadi), farshad.saki1992@aut.ac.ir (F. Saki).
https://doi.org/10.1016/j.resourpol.2022.102676
Received 11 November 2021; Received in revised form 26 February 2022; Accepted 14 March 2022
Available online 30 March 2022
0301-4207/© 2022 Elsevier Ltd. All rights reserved.
F.S. Namin et al.
Resources Policy 77 (2022) 102676
Fig. 1. Timeline diagram for development of MMS models.
2009). Another study by Alpay & Yavuz in 2009 presented a program
based on the AHP and Yager method for selecting underground mining
methods. Hierarchical analytical process and Yager method are decision
tools that can be used to select the best mining method by considering
the criteria of the case. Using the Yager model along with AHP to
consider uncertainties using fuzzy sets is one of the advantages of this
study (Alpay and Yavuz, 2009). The optimal mining method has been
reviewed for several mines according to effective criteria and using AHP
technique (Gupta & Kumar, 2012). In 2014, AHP and Yager’s methods
were used again to select the optimal underground mining method for
coal mines in Istanbul, and as a result, both decision-making models;
room and pillar methods with filling were selected as suitable options
(Mahmut Yavuz, 2014). Also, a research has been conducted in West
Virginia using the AHP method to improve common surface mining
practices and reduce the environmental damage caused by deforestation
(Nolan and Kecojevic, 2014). Furthermore application of decision
making methods in selecting different mining methods by AHP has been
presented (Stevanović, 2018, n.d.). The selection of mining method for
thick layers of coal is done using AHP method. According to the results
of AHP method, the highest score belongs to the method of retreating
longwall with filling (Yetkin and Özfırat, 2019). In 2020, ten mining
methods have been introduced as the primary selection options for the
Counterfield mine in Australia, which were ranked using the AHP
method. Finally, the block caving method was selected as the most
suitable option. According to these researches, it seems that the AHP
method is relatively simple in terms of computation as well as a practical
and applicable tool for selecting the mining method (Balt and Goosen,
2020).
Reviewing the articles that have used AHP method for MMS, showed
that in case studies that the weight of effective indices was not available,
this method would perform more appropriately and would be recom­
mended, as the weight of indices would be determined simply by AHP
method. The basis of AHP is the pairwise comparison of indices, which
leads to better results compared with other methods. If the number of
alternatives in a decision-making problem is “m” and the number of
criteria is “n”, then we will have a pairwise comparison matrix with n
dimension and n pairwise comparison matrix with m dimension. The
number of needed pairwise comparisons to determine the matrices are
obtained from equation (1):
[
]
[
]
n(n − 1)
m(m − 1)
N=
+n
(1)
2
2
has the highest compliance with the effective criteria. In recent years,
the use of decision-making models has become very popular. Different
decision-making models have been used, although their efficiencies
have not been compared in MMS.
The purpose of this study is to investigate the applications of
decision-making models in the selection of ore body mining methods. In
this review, the MCDM models used in choosing the mining method will
be presented. For this purpose, MCDM fuzzy and non-fuzzy models and
their applications in selection of mining methods will be discussed. In
this review, each MCDM technique, which has been recently used for
determination of mining methods will be presented. Also, we will focus
on the mines especially the type of minerals, selection of surface or
underground mining methods, and evaluation of the effective parame­
ters in decision making as a guidance for future research. Finally, a
conclusion of previous studies will be made to shed light on the route of
future studies.
2. Multi-criteria decision analysis on MMS (Fuzzy and crisp
approach)
2.1. Analytic hierarchy process (AHP)
The analytical hierarchy process (AHP) in solving decision problems
was first proposed by Thomas Saaty in 1980. In this method, complex
problems are analyzed graphically in a hierarchical manner. This
method is based on pairwise comparisons of criteria based on a scale
proposed by Thomas Saaty. From the pairwise comparison matrix, the
weights of the criteria and sub-criteria are calculated and the options are
prioritized. Decision making in this model is done in three stages of
hierarchical construction, calculating the weights and the rate of in­
compatibility (Saaty, 1977). In 2003, the decision support system was
designed by Yavuz and Alpay to select an underground mining method
by considering all the criteria in a knowledge base as well as researching
all the effects of different scenarios related to different criteria. AHP
method was used in the solution (Yavuz and Alpay, 2003). In 2007,
Yavuz and Alpay used group AHP decision making in a study to select a
mining method. At the first stage, according to the issue of selecting the
mining method, 36 criteria were analyzed in 6 main groups. At the
second stage, among the 36 available criteria, 18 criteria were selected
by experts using the AHP method and finally, for a case study, the block
caving and square set stoping method was determined (Serafettin Alpay
and Mahmut Yavuz, 2007). In 2008, Atai and Jalali used the AHP
method considering 13 criteria and developing a suitable mining
method for Jajarm bauxite mine. Among the six proposed mining
methods (sublevel stoping, mechanized cut and filling, traditional cut
and filling, step mining, stull stoping and shrinkage stoping), for this
deposit, the traditional cut and filling method was proposed as the
optimal method (Ataei et al., 2008a; Jamshidi et al., 2009). Musingwini
and Minnitt also used the AHP to select a method for mining platinum in
South Africa. Considering 8 criteria and 4 mining methods, finally the
Conventional breast mining method showed the highest score (Mus­
ingwini and Minnitt, 2008). In 2009, Jamshidi and Ataei again proposed
a model for selecting the optimal underground mining method using
AHP. In this model, the thickness parameter was identified as the most
important factor, followed by the RMR of the waist and the slope of the
mineral as the second important parameters. For Jajarm bauxite mine,
out of six mining methods, the traditional cut and filling method with 13
effective factors was introduced as the optimal option (Jamshidi et al.,
where, N is the number of pairwise comparisons in the problem, n is the
number of criteria and m is the number of alternatives. Higher number
of pairwise comparisons (judgments) leads to a longer time to reach the
results and greater overall error, although, by application of Expert
choice software the time of resolving will be lower and the Inconsistency
Ratio of the judgments will be controlled.
2.2. Fuzzy hierarchical analysis method
The concept of uncertainty has been considered indirectly in classical
AHP without the use of fuzzy sets. In fact, in this method, the concept of
being fuzzy is involved in determining the pairwise comparison matrices
by using linguistic variables. Therefore, by generalizing the AHP
method, methods are presented in which, fuzzy numbers are used to
express the predominance of elements. In 2008, karadogan et al.
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the ore category. Hence, it should be kept in mind that the probability
function is related to the random feature of the variables, and the fuzzy
degree of membership is associated with consistency and compatibility
to a characteristic (attribute). In fuzzy theory, the experiences and ideas
of the experts are considered and it is more flexible than the probability
theory. That is why it seems that the Monte Carlo Hierarchical method
has no proper application in MMS.
proposed the optimal mining method using the Fuzzy Analytical Hier­
archy Process (FAHP) technique for Siftlan mine near Istanbul in Turkey.
From the five proposed methods, the room and pillar method was
selected as the optimal method (Karadogan et al., 2008). Naqdehi et al.
have performed the optimal mining method for Jajarm bauxite mine.
Finally, the traditional cut and fill method was proposed (Naghadehi
et al., 2009). In another research, a two-stage algorithm including a
technical-operational hierarchical model as well as a hierarchical eco­
nomic model was proposed, taking the Nicholas method into account.
These models include some new criteria that are being added by the
Nicholas method; therefore, using FAHP, first, the mining options are
ranked based on the technical and operational hierarchical model and
then, most of these options are selected by the hierarchical economic
model (Azadeh et al., 2010). Karimnia and Begloo have proposed the
appropriate mining method for Qapluq salt mine. They used FAHP
technique to select the mining method (Karimnia and Bagloo, 2015). In
another research, with the aim of sensitivity analysis in decision making,
which leads to the selection of the appropriate method of underground
metal mining. The proposed model considered 16 criteria for selecting
the most appropriate mining method from seven methods. (Balusa and
Gorai, 2019b). In another paper, Factors affecting the selection of the
optimal method of underground mining have been described and
analyzed. Finally, considering the existing criteria, the Vertical Crater
Retreat (VCR) was selected as the most suitable underground mining
method (Bajic et al., 2020).
By combining the fuzzy logic and the AHP in resolving the MMS
problems, ambiguities can be considered. Given that the uncertainty of
exploratory data in the best condition is 15%, application of fuzzy
attitude in mining engineering will be indispensable. The fuzzy attitude
is a method for consideration of uncertainty in problem solving which,
reduces the error of judgments. That is why the application of fuzzy
attitude in MMS has been increased in recent years.
2.4. Technique for order performance by similarity to ideal solution ideal
(TOPSIS)
The method of similarity to the ideal solution was first proposed by
Huang and Yoon in 1981. In this method, the options are ranked based
on the similarity to the ideal solution, so that the more similar the option
to the ideal solution, the higher the rank will be. The steps performed in
this model to achieve the answer include determination and scaling the
decision matrix, evaluation of the effect of criteria weight on the deci­
sion matrix, determination of the ideal and negative-ideal solution,
calculation of the distance between the options from the ideal and
negative-ideal solution, calculation of the criteria of ranking the options
and finally, ranking the options (Tzeng and Huang, 2011). Atai et al.
Used the TOPSIS method, considering 13 criteria, to develop a suitable
mining method for Golbini deposit No. 8 in Jajarm, Iran. Six mining
methods have been considered for this mine, and finally the traditional
cut and filling method was selected as the most optimal method ((Ataei
et al., 2008a). A study has been conducted by Asadi et al. to create a new
model for selection of mining methods to achieve a sustainable pro­
duction and reduce environmental problems in the Tazareh coal mine.
After implementation of the model, the longwall method has been
selected as the most appropriate mining method (Asadi Ooriad, Yari,
Bagherpour and Khoshouei, 2018).
2.5. Fuzzy technique for similarity to ideal solution (FTOPSIS)
2.3. Monte Carlo Hierarchical analysis (MAHP)
The fuzzy TOPSIS method is an appropriate way to use linguistic
variables in decision making. The proposed model can be a suitable tool
for selecting the mining method. The fuzzy TOPSIS model has been
implemented by Samimi Namin et al. on two mines; Chahar gonbad and
Gol-e-Gohar Anomaly No. 3, and the open pit mining method has been
proposed for both. In another research, combining the FTOPSIS and
Fuzzy Analytical network proses (FANP) methods, the relative weight of
the criteria has been modeled and the use of these combined techniques
to determine the overall weight has been discussed. Finally, to show how
to use this model, it was run on one anomaly of Gol-e-Gohar and as a
result, the method of block caving was determined for this mine (Samimi
Namin et al., 2012). In another study, an integrated model based on
FAHP and FTOPSIS has been developed. FAHP was used to determine
the relative weight of the evaluation criteria for the selection of the
mining method (these weights have been determined for ranking the
options and selecting the most suitable option by the FTOPSIS method in
Anguran lead and zinc mine (Haji Yakchali et al., 2012). In 2017, using
the fuzzy method, the similarity to the ideal solution (fuzzy TOPSIS) was
considered to select the best mining method from the methods of square
set stoping, cut and filling, shrinkage and sublevel stoping in Kemer
Mehdi fluorine mine, with 14 criteria including thickness, storage slope,
grade distribution, etc. Finally, the shrinkage method was proposed as
the best mining method (Javanshirgiv and Safari, 2017). Also, using the
FTOPSIS model, the mining methods for Kakosa mine were ranked and
finally, the open pit method was selected as the best method (Kangwa
and Mutambo, 2019). For this purpose, a fuzzy technical-financial
method based on technical and finance budgeting criteria was devel­
oped according to numerical scoring method and two fuzzy
multi-criteria decision making techniques called FAHP and FTOPSIS.
The results showed that sublevel stoping was the most optimal mining
method for the selected case (Banda, 2020).
The weight of attribute cannot be determined using TOPSIS and
Monte Carlo methods are successfully used for complex nonlinear
systems with a high degree of uncertainty such as turbulence in fluids,
heterogeneous environments, cellular structures or systems with inde­
terminate inputs. (Ghiass, 2014). The development of a Monte Carlo
simulation to select the optimal mining method using effective criteria
and considering the judgments of decision makers has led to proposing
the most appropriate extraction method for Jajarm bauxite mine using a
combination of Monte Carlo simulation and AHP method the (Ataei
et al., 2013).
In Monte Carlo Hierarchical method, the attitude of probabilities is
used for expression of uncertainty. This is very important to use fuzzy
theory instead of probability theory, because the uncertainty in effective
criteria in MMS has no random nature and no probability feature. The
probability theory is only applicable for a special state of uncertainty
which, is resulted from the random feature of the nature dominant on
those phenomena. There are states of uncertainty that have no root in
the random nature of the phenomena; but, are related to the insufficient
data and ambiguous or sometimes contradictory information (such as
exploratory data in mines or judgments of experts). This can be
explained with a simple example: if the cut of grade is considered 2.5 g/
ton, based on the classic logic, block with grades of 2.51, 2.55 and 2.60
g/ton will be categorized in the ore category. According to fuzzy logic,
the rate of belonging of a block to the community or category of ores or
waste is important. It means that the degrees of membership of these
blocks to the ore category are not the same. In the same example, when it
has been said the degree of membership of a specific block to the ore
category is 90%, it means that 90% of the block characteristics is in
accordance with the ideal block of the ore, and the uncertainty is in the
remaining 10%. However, in probability attitude, if we say that the
probability of belonging of a specific block to the ore category is 90%, it
means that 90% of the blocks with similar statistical condition belong to
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Fuzzy TOPSIS methods. The TOPSIS and FTOPSIS methods are not
appropriate for MMS. In the aforementioned studies, most of the at­
tempts were made to mix these methods with other methods to deter­
mine the weight of effective indices in MMS. Of course, it should be
mentioned that a method for determination of the weights of selection
indices of the extraction method, based on the previous studies on
minerals is presented in the second final of this paper.
2.8. TODIM method
The TODIM method (an acronym in Portuguese of interactive and
multiple attribute decision making) has been introduced in 1992. In this
method, m existing options are ranked according to n qualitative or
quantitative criteria. Criteria are usually classified into two types: profit
(positive) and cost (negative). After determining these criteria, using the
opinion of experts, the values related to each criterion will be deter­
mined and one of the criteria will be considered as a reference criterion.
Comparing the numbers of final mastery matrix, gives the ranking of
options (Gomes et al., 2009). Proposed models have been presented to
determine the best mining method in Gol-e-Gohar No.1 iron ore mine
and the obtained results have been compared with Gray and TODIM
decision-making methods in previous research works. Both
decision-making techniques introduced the open pit method as the most
suitable option and the square set stoping method as the worst option
(Dehghani et al., 2017).
A system was developed to select the submarine extraction method
based on technical feasibility, security status, economic benefits and
management complexity. Then, a combined model of fuzzy theory and
TODIM method was proposed. The results showed that the TODIM
method was the proposed reliable and stable combination for selecting
the optimal mining procedure in submarine deep gold mines (Weizhang
et al., 2019). In the other paper, ten multi-criteria decision-making
methods as well as their application, performance, advantages and
disadvantages of each method have been identified to solve the problem
of selecting the mining method. Finally, the cut and filling method was
chosen for Anguran lead and zinc mine (Baloyi and Meyer, 2020).
Finally, in 2020 considering the nature of input parameters and the type
of output parameters, the most appropriate decision-making method
was selected from the 12 available methods. The results obtained from
the model showed that TODIM and ELECTRE methods would be the
most appropriate and inappropriate decision-making methods, respec­
tively for selecting the mining method for this mine. Then, using the
TODIM method, cut and filling method and the room and pillar method
were proposed as the most suitable and unsuitable mining methods for
Anguran mine, respectively (Saki et al., 2020).
TODIM based on group decision making is meaningful to find out a
development method that not only considers the bounded rationality of
decision maker, but also overcome its computational complexity.
2.6. PROMETHEE multi-criteria decision-making method
The PROMETHEE multi-criteria decision-making model was first
proposed by Brans in 1986 and found many applications in the early
years. The motivation for presenting the PROMETHEE method was the
exaggerating use of a performance function for all criteria. In this
method, unlike other methods, the selection options are compared in
pair wise (Brans et al., 1986). In a study conducted by Samimi Namin
et al., the techniques in multi-criteria decision making have been clas­
sified into three categories: the value of measurement models, reference
level and objective models and ranking models, and three methods of
TOPSIS, AHP, PROMETHEE have been considered as examples of each
group. Also, a study was considered to select the method of mining of
different mines in Iran (Samimi Namin.et al., 2009. In another work, the
AHP and PROMETHEE methods have been used to select the best
method of underground mining for “Coka Marin” mine in Serbia. The
AHP method has been used to analyze the structure, select the mining
method and determine the weight of the criteria, and the PROMETHEE
method has been used to obtain the final rank and sensitivity analysis by
changing the weight (Bogdanovic et al., 2012). In article research using
geological criteria, three decision-making methods of AHP, PROM­
ETHEE and AHP-PROMETHEE have been used to select the mining
method for this mine. Considering eight criteria and four mining
methods, the method of sublevel caving was selected as the most optimal
one (Mijalkovski et al., 2013). Using two multi-criteria decision-making
methods (WPN, PROMETHEE), the mining method was selected for
bauxite mine. The results showed that the highest score belonged to the
traditional cut and filling method for this bauxite mine (Balusa and
Singam, 2017).
PROMETHEE is the most similar method to the decision making in
the human brain. It seems that by expansion of artificial intelligence,
selection of the mining method by PROMETHEE will be more popular.
The outlook of developing a learnable intelligent system for selection of
mining method according to the worldwide collected data based on the
fuzzy PROMETHEE is very bright and recommended.
2.9. VIKOR multi-criteria decision-making method
In 1988, Apricovich and Tzang introduced the VIKOR method (an
acronym in Serbia of multi criteria optimization and compromise solu­
tion), and in 2002, 2003, 2004, and 2007 developed the method. This
method, which is based on compromise planning of multi-criteria de­
cision-making issues, evaluates the issues with inappropriate and
incompatible criteria. In situations where the decision maker is not able
to identify and express the advantages of an issue at the time of its
initiation and design, this method can be considered as a suitable tool for
decision making. Gelvez and Aldana used the VIKOR method to select
the mining method in 2014. According to their research, the alternatives
was selected by using the numerical scoring technique. Then using the
AHP and VIKOR methods from 19 effective criteria selected by experts
and among the alternatives, the long wall was selected (Gelvez and
Aldana, 2014). A mobile application has been developed to select the
most convenient underground mining method for a mine using
well-known multi-criteria decision-making methods such as FMADM,
ELECTRE, VIKOR, TOPSIS and PROMETHEE. The developed mobile
application can perform the process of method selection by prioritizing
the options of underground mining methods to consider some
decision-making factors that are not considered in the usual approaches
of choosing underground mining methods (Iphar and Alpay, 2018). The
Hesitant Fuzzy Linguistic Gained (HFLG) and Lost Dominance Score
methods allow mine planning engineers to transfer their knowledge
2.7. MULYIMOORA decision-making method
The MULYIMOORA multi-objective optimization method was pro­
posed in 2004 by Brewerz and Zavadskas. This technique allows nonsubjective assessments due to the lack of obligation to use the weight­
ing method. In fact, unit less measures were used to in the ratio system to
solve weighting problems in previous optimization models (AHP,
ELECTRE, PROMETHEE, TOPSIS) (Brauers et al., 2012). The obtained
ratio was also used in the reference point method and the sum of these
two techniques was named Mora. Hence, a new decision-making
method, named MULTIMOORA was proposed. Feasibility studies have
been shown through an example to select the optimal mining method
using this new method, (Weizhang et al., 2018).
To resolve MMS problem, an extended multi-objective optimization
by ratio analysis plus the full multiplicative form (MULTIMOORA)
approach is studied. Multi-objective optimization method was applied in
MMS for the first time in this study. Multi-objective optimization is
mostly used for designing and optimization. MMS is a selection problem
and is recommended based on the input data and the nature of the
problem of MCDM methods.
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Fig. 2. Distribution of studies conducted on application of decision-making models in the selection of mining methods in different countries.
with others.
using HFLG in the underground mining method selection process for
more information. However, as can be seen in this study, the choice of
mining method for a lead and zinc mine located in China has been
presented (Fu et al., 2019). The selection of the best mining method for
bauxite mine was done using AHP and VIKOR methods. The AHP
method was used to determine the weight of the effective parameters
and the VIKOR method was used to select the options. The results
showed that the appropriate mining method for the specified criteria of
bauxite mine was the traditional cut and filling method (Chander et al.,
2018). The results of prioritization of seven mining methods from five
multi-criteria decision-making models have been compared by Balusa
and Gorai in 2019. The introduced models considered eight criteria for
evaluating the options and determined the weight of each criterion using
AHP method. The results showed that the VIKOR model presented the
compromise solution, i.e. the method of extracting the room and pillar
was the same as the cut and filling method (Balusa and Gorai, 2019a).
The VIKOR and TOPSIS are based on an aggregating function rep­
resenting closeness to the ideal solution. The VIKOR method develops a
compromise solution with an advantage rate to ranking. One of the
VIKOR limitation is choosing the final selection based on three in­
dicators: utility measure, regret measure and VIKOR index. This some­
times results in no final response when using VIKOR method in MMS.
However, it is not the same in TOPSIS.
VIKOR and TOPSIS introduce different forms of aggregating equa­
tion for ranking. The VIKOR method introduces VIKOR index, whereas
the TOPSIS method introduces closeness index. Furthermore VIKOR and
TOPSIS methods use different kinds of normalization to eliminate the
units of criterion. The TOPSIS method uses vector normalization and the
VIKOR method uses linear normalization. The highest ranked alterna­
tive by VIKOR is the closest to the ideal solution. However, the highest
ranked alternative by TOPSIS is not always closest to the ideal solution.
However, the TOPSIS method is recommended in comparison with
VIKOR.
3. Investigation of ore body type and location
In this section, the mines around the world that their selection of
mining method have been performed using MCDM methods, are
reviewed and a conclusion will be presented at the end. Fig. 2 shows the
distribution of studies in different countries. The reason of this investi­
gation is the effect of existing technology in different countries on the
selection of mining extraction method of MMS. This effect should be
always considered in application of MMS templates. For instance, while
applying UBC method presented in University of British Columbia,
Canada, the probability of selecting open stoping methods is higher than
other methods. It means that in scoring the indices, more attention has
been paid to open stoping methods compatible with the mining condi­
tion in Canada. One of the strength of the decision-making models in
comparison with previous methods is the possibility of application
compatible with the local condition. The strength of the present research
is the dispersion of the studies throughout the world, which is indicated
in Fig. 2, and will be presented in the following.
Furthermore, some of the most important research works in choosing
the mining method are presented in Table 1. In this table, the type of
mineral and the decision-making model used in each research is shown.
Table 2 shows multiple studies on the selection of mining methods using
the multi-criteria decision-making method. In order to review the
research on the choice of mining method, the studies are classified into
four groups of five years. As shown in Table 2, 36% of the studies have
been conducted in the recent five years. In this table, increase in
application of decision-making models in mining method selection in
the last 20 years is indicated. Also, the mines evaluated in different
research papers will be introduced and the type of mineral and their
results will be presented. One of the purposes of this study was to
determine the weight of effective indices in selection of MMS mining
methods for different ore body type. In this regard, most of the articles in
database have been published more recently which shows that the
presented weights at the end of the works were in concordance with the
modern mining technology and also, compatible with distribution of
mines throughout the world.
2.10. PIPRECIA-E decision model
The PIPRECIA-E method has been suggested to solve the problem of
selecting the mining method (Stanujkic et al., 2017). The starting point
for establishment of this method was the SWARA method (Keršuliene
et al., 2010). That is, PIPRECIA-E retains the good features of the
SWARA method and overcomes its shortcomings. Unlike the SWARA
method, the PIPRECIA-E method does not require prior sorting of
evaluation criteria, which makes this method more suitable for use in
group decision making. The application of the proposed method has
been demonstrated using 18 criteria and five methods of underground
mining. Finally, the sublevel caving method was selected as the best
mining method (Popović et al., 2019). Group decision making and using
the experience of different experts is the ability of this method compared
3.1. Introduction of investigated mines
3.1.1. Iran mines
3.1.1.1. Gol-E-Gohar Sirjan iron ore mine. Gol-E-Gohar iron ore mine
complex of Iran is located in Kerman province, with a longitude of 55◦
and 19′ east and a latitude of 7◦ north. Seven studies have been per­
formed on this mine using decision-making methods (FDM, FMADM,
AHP, FTOPSIS, TOPSIS, FANP, Gray, TODIM, etc.) and in the end, the
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Resources Policy 77 (2022) 102676
Table 1
Multi-Criteria Decision-Making (MCDM) research works on MMS.
Table 1 (continued )
Researcher (s)
Decision-making
Approach
Case study
Ore
Guray et al.
(2003)
Yavuz and Alpay
(2003)
Samimi Namin
et al. (2003)
Bitarafan and
Ataei (2004)
Alpay and Yavuz
(2007)
Fuzzy decision model
–
–
AHP
–
–
FDM
Iron
Yavuz and Alpay
(2008)
Samimi Namin
et al. (2008)
Karadogan et al.
(2008)
Ataei et al.
(2008a)
Ataei et al.
(2008b)
Musingwini and
Minnitt (2008)
Jamshidi et al.
(2009)
Alpay and Yavuz
(2009)
Samimi Namin
et al. (2009)
Naghadehi et al.
(2009)
Azadeh et al.
(2010)
Liu et al. (2010)
Gupta & Kumar
(2012)
Multicriterion
optimization
FTOPSIS
AHP
Gol-e-Gohar mine,
Iran
Gol-e-Gohar mine,
Iran
EskisehirKaraburun ore
mine, Turkey
Kayseri-Pinarbasi,
Turkey
Gol-e-Gohar mine,
Iran
Ciftalan mine,
Turkey
Jajarm mine, Iran
TOPSIS
Jajarm mine, Iran
Bauxite
AHP
Angelo Mine, South
Africa
Jajarm mine, Iran
Platinum
Chromite
AHP, TOPSIS &
PROMETHEE
FAHP
EskisehirKaraburun, Turkey
Gol-e-Gohar mine,
Iran
Jajarm mine, Iran
FAHP
Choghart mine, Iran
Iron
Unascertained model
AHP
Xinli Mine, China
Bergslagen, Sweden
Karnataka, India
Rajasthan, India
d’Alene, Idaho,
USA
Greens Creek Mine,
USA
Tabas mine, Iran
Gold
Iron
Gold
Copper
Silver and
Lead
Silver
Angouran mine,
Iran
Amasra mine,
Turkey
Coka Marin mine,
Serbia
Gol-e-Gohar mine,
Iran
Svinja Reka,
Macedonia
Angouran mine,
Iran
Jajarm mine, Iran
Cerro Tasajero
mine, Colombia
West Virginia, USA
Zinc, Lead
Qaleh-Zari mine,
Iran
Qapiliq mine, Iran
Copper
–
Iron
WPM & PROMETHEE
–
Gol-e-Gohar mine,
Iran
Kamar Mahdi mine,
Iran
India
TOPSIS
Tazareh mine, Iran
Coal
FMADM
AHP
FAHP
AHP
AHP & FMADM
Nourali et al.
(2012)
Haji Yakchali
et al. (2012)
Özfırat (2012)
HPVs
Bogdanovic et al.
(2012)
Samimi Namin
et al. (2012)
Mijalkovski et al.
(2013)
Shariati et al.
(2013)
Ataei et al. (2013)
Gelvez and
Aldana (2014)
Nolan and
Kecojevic
(2014)
Ghazikalayeh
et al. (2014)
Karimnia and
Bagloo (2015)
Kant et al. (2016)
Dehghani et al.
(2017)
Javanshirgiv and
Safari (2017)
Balusa and
Singam (2017)
AHP & PROMETHEE
FAHP & FTOPSIS
FAHP
FANP & FTOPSIS
AHP, PROMETHEE &
AHP-PROMETHEE
FAHP & FTOPSIS
MAHP
AHP & VIKOR
AHP
FAHP
FAHP
A review Research
Gray & TODIM
FTOPSIS
Researcher (s)
Asadi Ooriad
et al. (2018)
Iphar and Alpay
(2018)
Weizhang et al.
(2018)
Fu et al. (2019)
Iron
Chromite
Chander et al.
(2018)
Stevanović et al.
(2018)
Hezaimia et al.
(2019)
Balusa and Gorai
(2019a)
Chromite
Iron
Lignite
Bauxite
Balusa and Gorai
(2019b)
Weizhang et al.
(2019)
Kangwa and
Mutambo
(2019)
Yetkin and
Özfırat (2019)
Popović et al.
(2019)
Balt and Goosen
(2020)
Banda (2020)
Bauxite
Iron
Bauxite
Baloyi and Meyer
(2020)
Bajic et al. (2020)
Khalifa et al.
(2020)
Saki et al. (2020)
Decision-making
Approach
Case study
Ore
TOPSIS, VIKOR,
ELECTER,
PROMETHEE &
FMADM
MULTIMOORA
Kayseri Pınarbasi,
Turkey
Chromite
Kaiyang mine,
China
Huidong mine,
China
Tummalapalle
mine, India
Kostolac mine,
Serbia
Boukhadra mine,
Algeria
Tummalapalle
mine, India
Phosphate
Tummalapalle
mine, India
Sanshandao,
Chaina
Kakosa South,
Zambia
Uranium
AHP
–
Coal
PIPRECIA
Čukaru Peki, Serbia
AHP
Canterfild,
Australia
Konkola East mine,
Zambia
Angoran mine, Iran
Copper,
Gold
Coal
HLF-VIKOR & HLFTOPSIS
FAHP
AHP
UBC, Nickson
TOPSIS, ELECTHER,
PROMETHEE, VIKOR
& WPN
FAHP
Fuzzytheory, TODIM
FTOPSIS, TOPSIS
FTOPSIS & FAHP
VIKOR, TOPSIS,
PROMETHEE, SAW
and …
FAHP
UBC, NIKOLAS
TODIDM
lead-zinc
Uranium
Coal
Iron
Uranium
Gold
Copper
Copper
lead-zinc
Borska Reka, Serbia
Sukari, India
Copper
Gold
Angoran mine, Iran
lead-zinc
coal
Table 2
Frequency of research based on year.
Coal
Copper,
Lead, Zinc
Iron
–
Zinc, Lead
Year
Number
Present
Before 2005
2006–2010
2011–2015
2016–2021
Total
4
15
12
24
55
12
32
20
36
100
open pitting method has been proposed for this mine (Samimi Namin
et al. 2008, 2009, 2012 and Bitarafan and Ataei, 2004).
Bauxite
Coal
3.1.1.2. Choghart iron ore mine. Choghart mine is located 12 km
northeast of Bafgh, 125 km southeast of Yazd and 75 km southwest of
Bahabad and on the desert margin. Therefore, using the FAHP method,
mining alternatives were ranked based on hierarchical technical oper­
ational model, and then the most profitable mining method for this mine
was selected by hierarchical economic model. Finally, the open pit
method was chosen (Azadeh et al., 2010).
Coal
Salt
3.1.1.3. Jajarm bauxite ore mine. Jajarm bauxite deposit is the largest
bauxite deposit in the Middle East located 16 km northeast of Jajarm
city, North Khorasan province, Iran. Five studies have been performed
on this mine using decision-making methods (AHP, TOPSIS, FAHP,
MAHP), which presented the traditional cut and filling method at the
end of research (Ataei et al., 2008b, 2013; Jamshidi et al., 2009;
Fluorine
Bauxite
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Naghadehi et al., 2009).
3.1.3. Serbia mines
3.1.1.4. Tabas coal mine. Parvardeh area with a vastity of about 1200
km2 is located 75 km south of Tabas city. The thickness of the over­
burden in this mine varies from 100 to 600 m. The hierarchical prefer­
ence voting system method was used to select the mine mining method
of Tabas mine. Finally, the mechanized longwall method was chosen
(Nourali et al., 2012).
3.1.3.1. Coka Marin mine. An integrated approach that uses AHP and
PROMETHEE to select the best underground mining method has been
used for the Coka Marin mine in Serbia. The AHP was used to analyze the
structure of the mining method and determine the weight of the criteria,
as well as the PROMETHEE method to obtain the final rank and sensi­
tivity analysis with weight change. Finally the highest score belonged to
the shrinkage stoping method (Bogdanovic et al., 2012).
3.1.1.5. Qapluq salt mine. Qapluq salt mine is located approximately
45 km west of the city of Khoy in West Azerbaijan, Iran. The geological
structure of the mining area includes salt domes that are covered by
layers of clay and agglomerate 2–7 m thick. A study using the FAHP
decision-making method has proposed a stop and pillar method for this
mine(Karimnia and Bagloo, 2015).
3.1.3.2. Kostolac coal basin mine. The Kostolac mine is located in
Serbia. A Kostolac mine study has presented the result of an analytical
hierarchical process. The analysis included six criteria and two surface
mining methods. As a result, the open pit mining method was selected
for this mine as the largest coal mine in Serbia (Stevanović et al., 2018).
3.1.1.6. Kamar Mahdi II fluorine mine. Kamar Mahdi fluorine mine is
located 85 km southwest of Tabas, in South Khorasan province in
eastern Iran. Using the FTOPSIS method, the mining method has been
selected and the shrinkage stoping method has been considered as the
best method (Javanshirgiv and Safari, 2017).
3.1.3.3. Čukaru Peki mine. The Čukaru Peki mine is located in Serbia. In
a study, the PIPRECIA-E method has been used to select the mining
method. Finally, the sublevel caving method was selected as the best
method for this mine (Popović et al., 2019).
3.1.3.4. Borska Reka copper mine. The Borska Reka copper mine is
located in eastern Serbia. The FAHP decision method has been used to
select the mining method leading to the selection of Vertical Crater
Retreat (VCR) as the superior method (Bajic et al., 2020).
3.1.1.7. Tazareh coal mine. Tazreh mining area is located 70 km
northwest of Shahroud city and 45 km northeast of Damghan city of
Semnan province in Iran. Mining in this area has been going on since 30
years ago. In this mine, FTOPSIS method has been used and the longwall
method has been selected as the best mining method (Asadi Ooriad
et al., 2018).
3.1.4. Zambia mine
3.1.4.1. Konkola east ore body. Konkola copper deposit is located about
450 km northwest of Luzaka in Zambia. Based on deposit geotechnical
analysis and rock mass classification, TOPSIS method has been used to
select the optimal mining methods and fuzzy approach has been used to
determine the criteria and weight of the options. The fuzzy numbers for
the mine parameters used as input data in the decision-making model,
corresponded to the criteria required to select the mining method. Also,
using the fuzzy decision-making model, the mining methods were
ranked and finally, the open pit mining method was selected as the best
method for konkola east mine (Banda, 2020).
3.1.1.8. Angoran lead and zinc mine. Angoran lead and zinc mine is
located in Zanjan province, Iran. Four studies have been performed on
this mine using decision-making methods (FAHP, TOPSIS, TODIM and
etc.), which have proposed the cut and filling method at the end (Saki
et al., 2020; Baloyi and Meyer, 2020).
3.1.2. Tsurkey mines
3.1.2.1. Karaburun mine. The Karaburun chromite mine is located 120
km east of Eskişehir in northwestern Turkey. The mining method has
been selected using AHP and FMADM techniques as well as considering
36 criteria and 6 main groups. Finally, the square-set stoping method
was selected as the best method (Alpay and Yavuz, 2007).
3.1.5. China mines
3.1.5.1. Kaiyang phosphate mine. Kaiyang phosphate mine located in
China, has a fixed reserve of 1.08 billion tons. Accordingly, using the
MULTIMOORA as novel decision technique, the non-pillar contact
sectional filling method has been identified as the best mining method
(Weizhang et al., 2018).
3.1.2.2. Kayseri-Pinarba ş I mine. The Kayseri Pınarba ş I mine is located
in Turkey for which, the available information has been used to select
the mining method using five decision-making methods including
TOPSIS, PROMETHEE, ELECTRE, VIKOR, Fuzzy AHP. These methods
are presented as a cellphone application that can be easily used. Finally,
the sub-level stoping method was selected as the best for this mine
(Yavuz and Alpay, 2008).
3.1.5.2. Sanshandao gold mine. For Sanshandao gold mine in China, the
mining method has been selected using a combination of fuzzy theory
and TODIM methods. The room and pillar alternation upward level cut
and fill stoping method has been selected (Weizhang et al., 2019).
3.1.2.3. Çiftalan mine. The Çiftalan lignite underground coal mine is
located 35 km north of Istanbul, Turkey. For this mine the AHP and
Yager’s FMADM methods have been used and both chose the method of
Room and pillar with filling as the best mining method (Karadogan et al.,
2008).
3.1.5.3. Huidong lead-zinc mine. Huidong lead and zinc mine is located
in Yunnan, China. Three methods of Hesitant Fuzzy Linguistic Gained
and Lost Dominance Score (HFL-GLDS) Method and HFL-VIKOR and
HFL-TOPSIS have been studied for Huidong lead-zinc mine. Finally, the
HFL-GLDS decision-making method was used for determination of
mining method, and the upward horizontal stratified cemented filling
mining method was chosen as the best mining method (Fu et al., 2019).
3.1.2.4. Amasra coal mine. In a study conducted on Amasra coal mine,
the underground mechanization factors have been grouped into four
main headings. The headings include factors of production, geology,
rock mechanics and work safety; and then, the sub-criteria were defined
under the main headings. The main and sub-criteria were evaluated by
FAHP method and finally, the fully-mechanized method was selected for
Amasra coal mine (Özfırat, 2012).
3.1.6. India mines
3.1.6.1. Khetri Copper ore deposit, Rajasthan. Khetri Copper deposit is
the northern extension of the Proterozoic Aravalli–Delhi Fold zone. A
study has been implemented for evaluation of the application of AHP to
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Fig. 3. Percentage of studies conducted at the international level on application of decision-making models in the selection of mining methods.
select the appropriate stoping method from a set of options. At the end of
the study, the developed model supported the method of stoping below
the training level (Gupta & Kumar, 2012).
3.1.9. Algeria mine
3.1.9.1. Djebel Boukhadra iron ore mine. The Djebel Boukhadra mine is
located in eastern Algeria, 13 km from the Algerian-Tunisian border. In
this regard, the numerical scoring methods have been used for this mine,
and finally, the sublevel stoping method has been selected as the best
mining method (Hezaimia et al., 2019).
3.1.6.2. Tummalapalle mine. Tummalapalle mine is located in Cudda­
pah district of Andhra Pradesh, India. The slope of this area varies be­
tween 15 and 17◦ . It has been shown that using the FAHP method, the
first rank was assigned to the room and pillar method (Chander et al.,
2018 and Balusa and Gorai, 2019b).
3.1.10. Egypt mine
3.1.7. USA mines
3.1.10.1. Sukari gold mine. The Sukari gold mine is located in the
eastern desert of Egypt with a length of approximately 2300 m. For this
mine, several numerical scoring methods and decision models have been
used for determination of the proper mining method. Finally, the sub­
level stoping has been selected as the best mining method (Khalifa et al.,
2020).
3.1.7.1. d’Alene mine. The Coeur d’Alene mining area is located in
northern Idaho. For this mine the AHP decision method has been used,
and the cut and filling mining method has been selected (Gupta &
Kumar, 2012).
3.1.11. Sweden mine
3.1.7.2. Greens Creek mine. Hecla Greens Creek mine in southeastern
Alaska is one of the world’s primary mines. AHP has been used to select
the mining method, and the method of cut and fill has been chosen as the
best selection (Gupta & Kumar, 2012).
3.1.11.1. Bergslagen multi-metal mine. The Bergslagen area is located
150 km west of Stockholm, south of Sweden, which includes reserves of
Cu, Pb, Zn, Au and Ag. Using AHP technique, the sublevel stoping has
been selected as the best mining method (Liu et al., 2010).
3.1.7.3. West Virginia. In order to improve common surface mining
practices and reduce the environmental damages caused by overburden,
a research has been conducted in West Virginia. Using the AHP tech­
nique based on production, economy and environmental criteria, the
traditional mining cycle (drilling, charging, blasting and haulage) and
surface mining machine were compared and the optimal method was
selected. The design and methods used in this research included five
related modules: properties of overburden rock, explosion drilling,
loading and haulage, surface mining method and finally analysis of
selecting the optimal mining method by AHP technique. At the end of
this research, extraction with surface mining machine by traditional
cycle was preferred according to the considered criteria (Nolan and
Kecojevic, 2014).
3.2. Interpretation of results
In this section, the results of studying the region of mines will be
evaluated and interpreted. Fig. 3 shows the percentage of studies con­
ducted internationally to apply decision-making models in the selection
of mining methods. Among the countries of the world, Iran and Turkey
have the highest percentage for the issue of choosing the mining method
using decision-making techniques, and this shows the importance of this
subject for Iranian and Turkish researchers. One of the reasons for this
high attention to the choice of extraction and exploitation of new res­
ervoirs can be the need for domestic production and reliance of the
economy on domestic and mineral resources in recent years. Hence, the
results presented in the following are more on the basis of mining in
these two countries, although the results can be used in other countries.
Generally, it can be said that the results have the highest concordance
with the mining condition of developing countries. In these countries,
the priority is the open-pit mine; that is why in most of the studies, the
open-pit mine is the final choice. In addition, studying the mine indi­
cated that most of the studies have focused on the existing reservoir in
these countries and the basic minerals such as iron ore, coal, lead and
zinc.
3.1.8. Colombia mine
3.1.8.1. Colombia coal mine. This coal mine is located in Norte de
Santander, Colombia. Two decision methods of VIKOR and AHP have
been effective in choosing the mining method. Two methods of cut and
filling and longwall are identified as the best mining methods by VIKOR.
In AHP, the longwall method is selected as the superior method among
other methods. Finally, by analyzing the mining conditions, the method
of long-wall is determined (Gelvez and Aldana, 2014).
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Fig. 4. Articles categorized according to web of knowledge Quartile scores.
Fig. 5. Number of decision-making methods in selecting the mining method.
4. Papers validation by publisher
5. Results and discussion
In this section, the validity of the studies will be evaluated. The
validity of the results presented in the next section is related to the
validity of these studies. In the following, the scientific quality of the
studies has been reviewed (Fig. 4). The articles are categorized ac­
cording to the Quartile Scores in the web of knowledge (Q). Given that,
35% of articles is allocated to Q1, 28% to Q2, 28% to Q3 and 9% to Q4.
In this section, the results obtained from evaluation of research pa­
pers will be presented and discussed. Fig. 5 shows the number of
decision-making methods used in selecting the mining method, with
AHP and Fuzzy AHP method ranking with 34% in the first place, TOPSIS
and Fuzzy TOPSIS with 16% in the second place and PROMETHEE with
10% in the third place. Furthermore in the initial years MCDM methods
had a trend to the technique based on ideal solution (TOPSIS), but due to
the weights determination, more application and also their simple
Fig. 6. Number of research on fuzzy and crisp decision-making models in MMS.
9
F.S. Namin et al.
Table 3
Frequency of using effective criteria and sub-criteria in MMS.
Environmental issues
Economic factors
Geology
Technical and operational
Geometry
Specifications of rock mechanics
Weight Frequency Sub criteria
Weight Frequency Sub criteria
Weight Frequency Sub criteria
Weight Frequency Sub criteria
Weight Frequency Sub
criteria
Weight Frequency Sub criteria
4.2
24
Dilution
7.7
44
Ore Dip
6.1
35
3
17
Recovery
7.5
43
Ore Shape
5.8
33
Ore Rock Mass Rating
(RMR)
Hanging-wall RMR
2.5
14
Production rate
7.5
43
5.8
33
Foot-wall RMR
1.4
8
Mechanization
6.5
37
Ore
Thickness
Ore Depth
4.4
25
1.2
0.9
0.5
7
5
3
Flexibility
Productivity
Mining efficiency
4
4
1.2
23
23
7
0.5
0.5
0.5
3
3
3
Ventilation
Preparation rate
Skilled man
power
0.9
0.9
0.5
5
5
3
0.4
2
0.5
3
0.4
0.4
0.4
2
2
2
0.5
0.4
0.4
3
2
2
0.4
2
Difficulty of the
procedure
Out per man shift
Gravity follow
Span stand up
times
Selectivity
Ore Rock Substance
Strength (RSS)
Hanging-wall RSS
Foot-wall RSS
Foot-wall Rock Quality
Design (RQD)
Ore RQD
Hanging-wall RQD
Ore Uniaxial
Comprehensive
Strength (UCS)
Hanging-wall UCS
0.4
2
0.2
1
Methane problem
0.4
2
0.4
2
0.4
2
1.9
11
Safety and health 1.8
10
Capital cost
6.3
36
1.6
9
Subsidence
1.1
6
0.9
5
0.7
4
0.7
4
0.2
1
0.4
2
Environmental
impacts
Mine
reclamation
Operational
cost
Overall price
0.5
3
Labor cost
0.5
0.4
3
2
Ore Value
Pay back
Grade
distribution
Hydrogeology
condition
Climate of the
region
10
Foot-wall UCS
Ore joint condition
Hanging-wall joint
condition
Foot-wall joint
condition
Ore Fracture shear
strength
Hanging-wall Fracture
shear strength
Foot-wall Fracture
shear strength
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F.S. Namin et al.
Resources Policy 77 (2022) 102676
Fig. 7. Frequency of using effective criteria based on ore-body type.
application pairwise comparisons based models (AHP) have been used
instead. According to the previous researches, it seems that the decisionmaking models based on pairwise comparisons such as AHP, give more
appropriate results, and are more commonly used. The reason of this
popularity is the availability of the option of comparison between
indices and determination of the weight of indices by this method.
Determination of the weight of indices is an important issue in selection
of mining method, and will be discussed in the following according to
previous studies. Also, the ANP analytical network process decisionmaking model, which is based on pairwise comparisons similar to
AHP, has gained less attention in previous studies. In addition to AHP
advantages, this method considers the relationship between criteria and
their effects on each other. Since the criteria of MMS problem affect each
other, it is proposed to pay more attention to ANP in MMS future studies.
PROMETHEE method has also gained low attention such as ANP
method. In this method, pairwise comparisons is done between alter­
natives, and for preference of attributes, unlike previous methods,
different functions can be defined. These two advantages can be helpful
in resolving the MMS problems.
Decision methods can be divided into deterministic (crisp) and fuzzy
gropes. In Fig. 6, the articles were compared according to the fuzzy and
definite basis, and the results showed that higher number of articles
tends towards deterministic methods. Reviewing the researches indi­
cated that in 72% of the studies, the mining method was selected ac­
cording to decision-making models without considering the uncertainty.
Only in 28% of the articles, fuzzy decision-making methods were used,
while the uncertainty of information in MMS was high and should have
been considered. Studies show that the process of selecting a mining
method has ambiguity and uncertainty; however, its application is less
than expected for some reasons such as lack of sufficient software in this
field, lack of sufficient expertise of mining experts and lack of training.
There are more reasons that can be mention complexity and incom­
prehensibility of fuzzy method for the mining engineers.
As can be seen, recent research has confirmed the effectiveness of
fuzzy tools under complex uncertain assessments in MMS. Hence, the
next suggestion is the extension of application of fuzzy models in MMS
problems. Since, the judgments of the decision maker in MMS are along
with uncertainty (based on exploratory data), application of fuzzy
models is recommended. In the following, the results of the study on the
considered criteria in articles with the subject of MMS are presented.
These results can help mining engineers in selection of the mining
method. In MMS with same ore-body (rock type), equal criteria and
parameters have not been used. Also, the weight of a defined criterion
has been different in various mines and is determined according to the
condition of each mine. According to literature review, 51 effective
indices in selection of mining method were identified. The frequency of
application of mining method selection criteria and the determined
weight of each index are presented in percent in Table 3. Researchers
can use these weights in selection of mining methods in their future
studies. These weights can be combined with the weights extracted from
expert system.
In order to study the effective criteria in MMS, these criteria were
divided in six main categories (Specifications of rock mechanics, Ore
geometry, Technical/operational parameters, Geology, Economic fac­
tors and Environmental issues). In Table 3, the effective criteria and subcriteria in selecting the mining method and the number of repeats of
each criterion in the articles are evaluated. Studies indicate that rock
mechanic (geo-mechanical) parameters with 37% repetition in more
than 90% of articles is assigned as the most applicable, and the geo­
metric and technical/operational parameters with 26% and 21% repeats
are assigned the second and third ranks, respectively. Also, the fact that
what criteria are considered in what mineral is very important. In
Table 4
Weight of effective criteria in selection of mining method based on ore type (percent).
Criteria
Iron
Copper
Gold
Lead and Zinc
Coal
Bauxite
Chromite
Salt
Fluorine
Phosphate
Uranium
Specifications of rock mechanics
Ore geometry
Technical and operational parameters
Geology
Economic factors
Environmental issues
47
26
16
7
1
2
41
24
17
9
7
2
23
15
42
8
12
Nil
33
25
24
7
11
1
29
28
24
5
11
3
35
31
27
7
Nil
Nil
49
28
9
9
2
2
60
40
Nil
Nil
Nil
Nil
64
21
0
7
7
Nil
14
18
41
9
14
5
35
31
23
8
Nil
4
11
F.S. Namin et al.
Resources Policy 77 (2022) 102676
addition, weight of the indices for different ore type were evaluated. The
results are presented concisely in Fig. 7.
According to Fig. 7 in the reviewed articles, geo-mechanical, geo­
metric and Technical/operational parameters have been assigned as the
most frequent parameters in the choice of mining method, as well as orebody type such as iron, bauxite and coal, which were the most investi­
gated and assigned the first to the third rank. Geometry and rock me­
chanics were the most important and repeated parameters in iron ore
mining method selection. Technical and operational parameters were
the most important parameters in bauxite mine. Also, the economic
criteria were the most important in coal mines. Also, in environmental
parameters, the mineral lead and zinc have been highly studied and
need more attention. At the end of this review paper, weight of criteria
can be determined for different ore type (based on Table 3 and Fig. 6).
The final weight of effective criteria in selection of the mining method
based on different ore type in presented in percent in Table 4.
Table 4 presents important results for mine engineers and indicates
the relative importance of effective parameters in MMS.
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6. Conclusion
One of the important points in mine exploitation is choosing the
mining method. If the choice of mining method is not determined
correctly, the mine exploitation will encounter many problems. Criteria
Such as economic factors (mining cost, amount of investment, etc.),
technical factors (mine recovery, dilution, mechanization, etc.) and
other factors are affecting the choice of mining method. Therefore,
higher sensitivity and precision should be dedicated to choosing the
mining method. In this study, 55 articles, entitled “mining method se­
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models were reviewed from 18 journals. Studies have been done on 32
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fuzzy decision-making methods have gained more attention in recent
years. As a conclusion, it is suggested to make and present a global fuzzy
knowledge database for MMS for different ore body. Finally, a guide for
determination of weight of criteria in selection of mining method for
different ore type is proposed.
Credit author statement
Farhad Samimi Namin: Supervision, project administration,
Conceptualization, Methodology, Investigation Resources, Data valida­
tion, Writing – original draft, Writing – review & editing.
Aliakbar Ghadi: Conceptualization, Investigation, Writing – review
& editing.
Farshad Saki: Validation, Data curation, Excel Software tool, Formal
analysis, Writing – original draft.
All authors have read and agreed to the published version of the
manuscript.
12
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