Ranking the strategies of mining sector through ANP and topsis in a SWOT framework/Gavybos sektoriaus strategiju rangavimas taikant ANP, TOPSIS ir SSGG metodus.

Azimi, Reza ; Yazdani-Chamzini, Abdolreza ; Fouladgar, Mohammad Majid 等

1. Introduction

Organizations today deal with unprecedented challenges and opportunities in carrying out their vital mission. Managers always look for comprehensive picture of present situation of the organization and a clear understanding of its future organization. For this reason, they need background information of strengths, weaknesses, opportunities, and threats (SWOT) situation of the organization in order to invest the challenges and prospects of adopting organization. SWOT analysis is an effective framework for an organization (or a company) that helps to address the effectiveness of a project planning and implementation (Taleai et al. 2009; Podvezko 2009; Podvezko et al. 2010; Diskiene et al. 2008). SWOT analysis is used in different sectors such as maritime transportation industry (Kandakoglu et al. 2009; Ghazinoory, Kheirkhah 2008; Kheirkhah et al. 2009; Maskeliunaite et al. 2009), technology development (Ghazinoory et al. 2009, 2011), device design (Wu et al. 2009), food microbiology (Ferrer et al. 2009), Hazard Analysis Critical Control Point (Sarter et al. 2010), Environmental Impact Assessment (Paliwal 2006; Medineckiene et al. 2010), tourism management (Kajanus et al. 2004).

However, the factors that can affect the SWOT are complex and often conflicting. One way to overcome the problem of evaluation performance with regard to various factors is the use of multiple criteria decision making (MCDM). The assumption of independence of criteria is not always correct because in real world the criteria are often dependent with each other. Analytical network process (ANP) is an appropriate tool in order to model complex problems with all kinds of relationship, dependency and feedback in the model and draws a systematical figure of the decision making problem. Likewise, TOPSIS technique is a suitable tool to evaluate alternatives.

In this paper, we applied the SWOT analysis and two multi-attribute evaluation methods that are called the analytic network process (ANP) and TOPSIS techniques to rank the strategies of Iranian mining sector. Iranian mining sector has a critical role in Iran's economy. This sector had exports reaching $8.13 billion in 2009-2010, accounting for about 32 percent of the country's non-oil exports (1). This level of export of minerals marked 45 percent of total exports in the industrial and mine sector. Based on the fifth development plan, this sector should represent about 1.6% of GDP (Gross Domestic Product). For achieving the aim, it is necessary to suitable strategies be determined and their priorities in order implement should be evaluated.

The remainder of this paper is organized as follows. The SWOT analysis is explained in section 2. Then in Section 3, ANP method is introduced. TOPSIS technique is defined in section 4. In section 5, we define probable mining strategies in Iran. The evaluation of mining strategies and the steps of proposed method are summarized in section 6. And finally section 7 concludes the paper.

2. The SWOT analysis

The SWOT analysis has its origins in the 1960s (Kandakoglu et al. 2009). It is an environmental analysis tool that integrates the internal strengths/weaknesses and external opportunities/threats.

This method is implemented in order to identify the key internal and external factors that are important to the objectives that the organization wishes to achieve (Houben et al. 1999). The internal and external factors are known as strategic factors and are categorized via the SWOT analysis. Based on the SWOT analysis, strategies are developed which may build on the strengths, eliminate the weaknesses, exploit the opportunities, or counter the threats (Kandakoglu et al. 2009).

SWOT maximizes strengths and opportunities, and minimizes threats and weaknesses (Amin et al. 2011), and transforms the identified weaknesses into strengths in order to take advantage of opportunities along with minimizing both internal weaknesses and external threats. SWOT can provide a good basis for successful strategy formulation (Chang, Huang 2006).

According to the capability and efficiency of the SWOT analysis, this technique is applied to different aspects of strategic management. Nikolaou and Evangelinos (2010) employed SWOT analysis for environmental management practices in Greek Mining and Mineral Industry, their stated policy recommendations both for the government and industry which, if adopted, could facilitate improved environmental performance. Chang and Huang (2006) used SWOT analysis to assess the competing strength of each port in East Asia and then suggest an adoptable competing strategy for each. Stewart et al. (2002) employed SWOT analysis in order to present a strategic implementation framework for IT/IS projects in construction. Terrados et al. (2007) developed regional energy planning through SWOT analysis and strategic planning tools, they proved that SWOT analysis is an effective tool and has constituted a suitable baseline to diagnose current problems and to sketch future action lines.

Quezada et al. (2009) used a modified SWOT analysis in order to identify strategic objectives in strategy maps. Zaerpour et al. (2008) proposed a novel hybrid approach consisting of SWOT analysis and analytic hierarchy process. Misra and Murthy (2011) developed a SWOT analysis of Jatropa with specific reference to Indian conditions and found that Jatropa indeed is a plant which can make the Indian dream of self-sufficiency in energy-a reality. Chang et al. (2002) applied SWOT analysis in order to forecast the development trends in Taiwan's machinery industry. Wang and Hong (2011) proposed a novel approach to strategy formulation, which employs the theory of competitive advantage of nations (a revised diamond model), SWOT analysis and strategy matching using the TOWS matrix and competitive benchmarking. Leskinen et al. (2006) used SWOT analyses to form the basis for further operations that were applied in the strategy process of the forest research station. Halla (2007) employed SWOT analysis for planning strategic urban development. Taleai et al. (2009) proposed a combined method based on the SWOT and analytic hierarchy process (AHP) in order to investigate the challenges and prospects of adopting geographic information systems (GIS) in developing countries. Leung et al. (2011) developed a SWOT dimensional analysis technique which is able to integrate the strengths and weaknesses of overseas real estate developers and also the opportunities and threats found in the market for formulating their strategic plans and market positions.

3. Analytical network process (ANP)

Analytical hierarchy process (AHP) was introduced by Saaty (1980) that is a mathematical technique for multi-criteria decision making. This technique is based on pairwise comparison matrix.

ANP is the general form of the analytic hierarchy process (AHP), which is introduced by Saaty (1996) in order to solve problems involving interaction and feedback among criteria or alternative solutions. This method is able to consider network structures because many real world problems cannot be structured hierarchically. ANP is a general tool that is helpful in assisting the mind to organize its thoughts and experiences and to elicit judgments recorded in memory and quantify them in the form of priorities (Saaty, Vargas 2006). This method is applied to multi-criteria decision making (MCDM) in order to release the restriction of hierarchical structure.

Fig. 1 illustrates the difference between hierarchy and network structures. As shown in Fig. 1, a hierarchy is a linear top down structure and network is a non-linear structure that spreads out in all directions.

ANP can be described in the following steps (Chung et al. 2005):

Step 1: Model construction and problem structuring: The derivation of the weights for all n components [C.sub.n] regarding the dependencies in relevance to an overall criterion, which can be elicited based on expert knowledge.

Step 2: Pair-wise comparison matrices and priority vectors: decision elements at each component are compared Pair-wise with respect to their importance towards their control criterion, and the components themselves are also compared pair-wise with respect to their contribution to the goal. The relative importance values are determined by using the Saaty's (Saaty 1980) 1-9 scale (Table 1).

Step 3: Supermatrix formation: the concept of supermatrix is similar to the Markov chain process that Saaty has developed it to synthesize ratio scales (Saaty 1996). Let the components (clusters) of a decision system be [C.sub.h], h = 1, ... n, and let each component h have [m.sub.h] elements, denoted by [e.sub.h1], [e.sub.h2], ..., [e.sub.hmn]. The influence of a set of elements belonging to a component, on any element from another component, can be represented as a priority vector by applying pair-wise comparisons in the same way as the AHP.

[FIGURE 1 OMITTED]

These priority vectors are grouped and located in appropriate positions in a supermatrix based on the flow of influence from a component to another component, or from a component to itself as in the loop. A standard form of a supermatrix is as follows (Liou et al. 2007).

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Where [W.sub.ij] is the principal eigenvector of the influence of the elements compared in the jth component to the ith component. In addition, if the jth component has no influence to the jth component, then [W.sub.ij] = 0. The form of the supermatrix relies on the variety of its structure. For instance, if assume there are two cases involve four components with different structures as shown in Fig. 2. Based on Fig. 2, the supermatrix can be formed as:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

The eigenvector for each column component, is multiplied by all the elements from the first component to the last component of that column. In this way, the component in each column of the supermatrix is weighted. The weighted supermatrix should be raised to the power of 2k + 1 (k is an arbitrarily large number) in order to converge the importance weights (Saaty 1996), because raising a matrix to exponential powers gives the long-term relative influences of the elements on each other.

[FIGURE 2 OMITTED]

Step 4. Selection of the best alternatives: If supermatrix only includes components that are interrelated, additional calculations must be made to obtain the overall priorities of the alternatives. The alternative with the largest weight should be selected, as it is the best alternative as determined by the calculations made using matrix operations.

4. TOPSIS approach

TOPSIS approach was developed by Hwang and Yoon (1981). This approach is used when the user prefers a simpler weighting approach. TOPSIS technique is based on the concepts that the chosen alternative should have the shortest distance from the ideal solution, and the farthest from the negative ideal solution. The usual TOPSIS approach has been applied for ranking construction and development alternative solutions since 1986 (Zavadskas 1986; Kalibatas et al. 2011; Tupenaite et al. 2010; Zavadskas et al. 1994, 2010; Jakimavicius, Burinskiene 2009; Liaudanskiene et al. 2009; Kucas 2010). Evaluation of ranking accuracy of TOPSIS was performed by Zavadskas et al. (2006). Modified method applying Mahalanobis distance was proposed by Antucheviciene et al. (2010). TOPSIS is defined as follows (Opricovic, Tzeng 2004):

Step 1: Normalize the decision matrix. The normalized value ([r.sub.ij]) is calculated as:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (1)

Step 2: Multiply the columns of the normalized decision matrix by the associated weights to generate the weighted normalized decision matrix. The weighted normalized value ([v.sub.ij]) is calculated as:

[v.sub.ij] = [w.sub.i][r.sub.ij], j = 1,2, ..., J; i = 1, 2, ..., n, (2)

where [w.sub.i] is the weight of the ith criterion, and

[[summation].sup.n.sub.i=1] [w.sub.i] = 1. (3)

Step 3: Determine the ideal and negative-ideal solutions through Eqs. (4) and (5).

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (4)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (5)

Where I' is associated with benefit criteria, and I" is associated with cost criteria.

Step 4: Measure distances from positive and negative ideal solutions using the n-dimensional Euclidean distance. The distance from positive ideal solution is:

[D.sup.*.sub.j] = [square root of [n.summation over (i=1)] [([v.sub.ij] - [v.sup.-.sub.i]).sup.2]], j = 1, 2, ..., J. (6)

Similarly, the distance from negative ideal solution is:

[D.sup.-.sub.j] = [square root of [n.summation over (i=1)] [([v.sub.ij] - [v.sup.-.sub.i]).sup.2]], j = 1, 2, ..., J. (7)

Step 5: Calculate the relative closeness to the ideal solution. The relative closeness of alternative [A.sub.j] with respect to [A.sup.*] is defined as:

[C.sup.*.sub.j] = [D.sup.-.sub.j]/([D.sup.-.sub.j] + [D.sup.*.sub.j]), j = 1, 2, ..., J. (8)

Step 6: Rank the preference order.

5. Case study

Mining is one of the most activities so that other activities such as manufacturing, construction, and agriculture, could not exist without raw mineral production. Mining plays a leading social-economic role in Iran. At its various stages--from exploration to production and selling--it generates a significant number of jobs and income for the country. Due to the rising demand for raw minerals by the industrial countries and most rapidly growing economies, mining is becoming increasingly important.

Iran is a country located in the Middle East with a non-federated governmental system. Iran is divided into thirty provinces.

Iran is one of the most important mineral producers in the world, ranked among 15 major mineral rich countries, 37 billion tons of proven reserves and more than 57 billion tons of potential reservoirs. Iran has one of the world's largest zinc reserves and second-largest reserves of copper. It also has significant reserves of iron, uranium, lead, chromium, manganese, coal and gold.

According to the importance of mining sector, at the end of Iran's fifth development plan, Iran should produce 31492.5, 480813.4, 3420, 110, 155, 360, 361, and 771 tons of crude steel, iron concentration, coal concentration, cement, building stone, zinc, copper (Cathode), and aluminum, respectively. For this reason, Iran's ministry of industries and mines should assign the feasible strategies and ranks the extracted strategies.

6. The implementation of proposed model

The proposed model of this paper uses an integrated method of the SWOT analysis, ANP, and TOPSIS to provide a framework for ranking the Iranian mining strategies. In order to implement the model, three stages are proposed: (1) the SWOT analysis of the Iranian mining sector is discussed and feasible strategies are determined, (2) then the ANP approach is applied to obtain the weight of the SWOT factors, and (3) finally, the TOPSIS technique ranks the Iranian mining strategies.

In the first stage, the possible strategies are determined by decision-making team in a framework of the SWOT analysis. In the second stage, the importance weights of main and sub-criteria are determined by decision-making team from high level managers in the template of the AHP questionnaire. The decision making team contains of twelve experts with high degree of knowledge in the field of management and mining. In this phase, the weights of criteria are obtained by pairwise comparison matrixes constructed by decision-making team through asking which is more important based on the scale provided in Table 1. The values obtained from individual evaluations are converted into final pairwise comparison matrix in order to find a consensus on weight of main and sub-criteria. In the last stage, strategies are ranked in descending order by TOPSIS method. In the first step of this phase, experts were asked to provide a set of crisp values within a range from 1 to 10 that represents the performance of each mining strategy with respect to each evaluation criteria. After forming decision making matrix, the computations of TOPSIS method is accomplished. In the last step of this stage, ranking of alternatives is carried out in descending order and the optimal strategy is selected. Schematic diagram of the proposed model for ranking the strategies is provided in Fig. 3.

The data of the SWOT analysis are based on the aggregate mining strategy reports of the ministry of industries and mines. The term 'strengths' contains advantages and benefits from the adoption of strategic management practices. In order to help the explorations of strengths, some typical questions should be answered such as what the benefits of such practices are, what strategic management practices can do well. Similarly, weaknesses would encompass agents and parameters that are difficulties in the efforts of companies to accept any strategic management practices. Some important questions could be what is not done appropriately, what should be better and what should be avoided.

[FIGURE 3 OMITTED]

Moreover, opportunities may include external benefits for companies from the acceptance of strategic management practices. Some relevant questions are what future benefits may take place for companies, what competitive advantages companies will gain and what changes may occur in consumer demands. Finally, threats may encompass future problems and difficulties from the prevention of implementing any strategic management practices. The basic parameters of the SWOT analyses are fall into two categories: external and internal. External category contains strengths and opportunities and internal category encompasses weaknesses and threats.

We prepared a list of strengths, weaknesses, opportunities, and threats, and then had an interview with the experts in mining strategies of Iran to modify the list. The results of the SWOT analysis based on expert knowledge are presented in Table 2.

As shown in Table 2, six strategies are earned from the SWOT analysis. These strategies in order to implement should be ranked because of the lack of finance and time as two limitations. For this reason, we applied the ANP technique and the TOPSIS approach in order to obtain the weight of SWOT factors and prioritize strategies respectively.

The proposed model is defined as follows:

Stepl: The hierarchy and network model proposed in this study for SWOT analysis is composed of four levels, as shown in Fig. 4. The goal (best strategy) is indicated in the first level, the criteria (SWOT factors) and subcriteria (SWOT sub-factors) are found in the second and third levels respectively, and the last level is composed of the alternatives (alternative strategies).

[FIGURE 4 OMITTED]

The supermatrix of a SWOT hierarchy with four levels is as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

Step 2: Assuming that there is no dependence among the SWOT factors, pairwise comparison of the SWOT factors using a 1-9 scale is made with respect to the goal. The importance weights of the criteria determined by twelve decision-makers that are obtained through Eq. (9) are shown in Table 3. The group consistency ratio (GCR) (Escobar et al. 2004) is available in the last row of the matrix.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (9)

where [x.sub.ij] is the crisp weight of each criterion that are determined by all experts, k is the number of experts (here, k is equal to 12).

Step 3: Inner dependence among the SWOT factors is extracted by analyzing the impact of each factor on every other factor using pairwise comparisons. As mentioned, existence of dependence among factors can be modeled through the ANP approach. Based on the SWOT analysis, the dependences among the SWOT factors are determined that are shown schematically in Fig. 5.

[FIGURE 5 OMITTED]

With respect to the inner dependencies shown in Fig. 5, pairwise comparison matrices are formed for the SWOT factors as presented in Tables 4, 5, 6 and 7 using Eq. (9). Based on the computed relative importance weights, the inner dependence matrix of the SWOT factors ([W.sub.2]) is generated. As each factor of the SWOT is affected by two other factors, so that; S factor is affected by W and O factors, W factor is affected by S and T factors, O factor is affected by T and S factors, T factor is affected by W and O factors.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

Step 4: The interdependent weights of the SWOT factors are calculated by Eq. (10) (Yuksel, Dagdeviren 2007) as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (10)

The results change from 0.49 to 0.38, 0.21 to 0.3, 0.13 to 0.19, and 0.15 to 0.13 for the priority values of factors S, W, O and T, respectively. As observed in the results obtained for the factor weights are different significantly.

Step 5: The local weights of the SWOT sub-factors are calculated using the pairwise comparison matrix. The pairwise comparison matrices, which are weighted by twelve experts and then are calculated by Eq. (9), are presented in Table 8.

Step 6: The overall weights of the SWOT sub-factors are calculated by multiplying the interdependent weights of SWOT factors obtained in Step 4 with the local weights of SWOT sub-factors found in Step 5. The computations of [w.sub.sub-factors (global)] vector are provided below. The rank of global sub-factors is shown in Fig. 6.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

[FIGURE 6 OMITTED]

Step 7: At this step of the proposed model, the team members were asked to establish the decision matrix by comparing alternatives under each of the SWOT sub-factors, a sample of decision matrix is presented in Table 9. Based on the responses of twelve experts, and using Eq. (9) the obtained results are as shown in Table 10.

Step 8: After forming the decision matrix, the normalized decision matrix is established with Eq. (1) as depicted in Table 11. Then, by multiplying the result of normalized decision matrix and obtained weighted for sub-factors in step 6, the weighted decision matrix is calculated as shown in Table 12. According to S1, S2, S3, O1, O2, O3, and O4 criteria are benefit criteria, and Wn1, Wn2, Wn3, T1, T2, T3, and T4 are cost criteria, the positive ideal and negative ideal solutions are defined by Eqs. (4), (5) as presented in two last rows of Table 12.

Step 9: The distance of each alternative from [D.sup.*] and [D.sup.-] can be currently calculated using Eq. (6) and (7). Finally, TOPSIS solves the similarities to an ideal solution by Eq. (8). In order to perceive what has been mentioned an example is presented as follows:

[D.sup.*.sub.1] = [square root of [(0.05 - 0.06).sup.2] + [(0.12 - 0.12).sup.2] + ... + [(0.01 - 0.01).sup.2] + [(0.01 - 0.01).sup.2]] = 0.0217,

[D.sup.-.sub.1] = [square root of [(0.05 - 0.04).sup.2] + [(0.12 - 0.05).sup.2] + ... + [(0.01 - 0.04).sup.2] + [(0.01 - 0.04).sup.2]] = 0.1674.

As a result,

[CC.sub.1] = [D.sup.-.sub.1]/[D.sup.*.sub.1] + [D.sup.-.sub.1] = 0.0217/0.1674 + 0.0217 = 0.0499.

Similar calculations are done for the other alternatives and the results of TOPSIS analyses are summarized in Table 13. According to Cj values, the ranking of the alternatives in descending order are A1, A5, A6, A2, A3 and A4. Proposed model results indicate that A1 is the best alternative with CC value of 0.855. The rank of alternatives is presented schematically in Fig. 7.

[FIGURE 7 OMITTED]

7. Conclusions

In this study, we applied an integrated model of the SWOT analysis and ANP approach and TOPSIS technique. The SWOT analysis constructs a framework, and the weights of SWOT factors and alternatives are obtained via ANP and TOPSIS respectively. The SWOT analysis was used in order to define strategies for Iranian mining sector. The SWOT analysis determined six strategies in order to implement in Iran. The MCDM methods have recognized wide applications in the solution of real world decision making problems. ANP is the preferred technique for obtaining the criteria weights and performance ratings when there is interdependence of characteristics. TOPSIS is a useful tool for prioritizing alternatives. The results show that A1 (0.885) has the highest weighting. From this result, decision makers or authorities should improve the ability of exploitation and production. Finally, we recommend that decision makers of mining industries can use this model to evaluate their activities for development or investment purposes.

doi: <DO>10.3846/16111699.2011.626552</DO>

Acknowledgement

The authors would like to thank the personnel of ministry of Iranian industries and mines.

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Reza Azimi [1], Abdolreza Yazdani-Chamzini [2], Mohammad Majid Fouladgar [3], Edmundas Kazimieras Zavadskas [4], Mohammad Hossein Basiri [5]

[1] Chief Manager of Exploration Department, Ministry of Industry, Mine and Trade, Shahid Kalantari St, Ostad Nejatollahi Ave, Ferdosi SQ, Tehran, Iran

[2,3] Fateh Research Group, Department of Strategic Management, Milad No. 2, Artesh, Aghdasieh, Tehran, Iran

[4] Faculty of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

[5] Faculty of Science and Engineering, Tarbiat Modares University, Cross of Jalale Ale Ahmad and Chamran Highway, Tehran, Iran

E-mails: [1] [email protected]; [2] [email protected]; [3] [email protected]; [4] [email protected] (corresponding author); [5] [email protected]

Received 31 May 2011; accepted 02 September 2011

(1) www.iran-daily.com

Reza AZIMI. Master of Science of industrial engineering, Chief Manager of Exploration Department, Ministry of industry, mine and trade, Tehran-Iran. Author of 5 research papers. In 2007 he graduated from the Science and Engineering Faculty at Azad University of Arak, Arak-Iran. His research interests include decision making, strategic management, modeling, and fuzzy logic.

Abdolreza YAZDANI-CHAMZINI. Master of Science in the Dept of Strategic Management, research assistant of Fateh Reaserch Group, Tehran-Iran. Author of more than 20 research papers. In 2011 he graduated from the Science and Engineering Faculty at Tarbiat Modares University, Tehran-Iran. His research interests include decision making, forecasting, modeling, and optimization.

Mohammad Majid FOULADGAR. Master of Science in the Dept of Strategic Management, Manager of Fateh Reaserch Group, Tehran-Iran. Author of 10 research papers. In 2007 he graduated from the Science and Engineering Faculty at Tarbiat Modares University, Tehran-Iran. His interests include decision support system, water resource, and forecasting.

Edmundas Kazimieras ZAVADSKAS. Prof., Head of the Department of Construction Technology and Management at Vilnius Gediminas Technical University, Vilnius, Lithuania. He has a PhD in Building Structures (1973) and Dr Sc. (1987) in Building Technology and Management. He is a member of the Lithuanian and several foreign Academies of Sciences. He is Doctore Honoris Causa at Poznan, Saint-Petersburg, and Kiev universities as well as a member of international organisations; he has been a member of steering and programme committees at many international conferences. E. K. Zavadskas is a member of editorial boards of several research journals. He is the author and co-author of more than 400 papers and a number of monographs in Lithuanian, English, German and Russian. Research interests are: building technology and management, decision-making theory, automation in design and decision support systems.

Mohammad Hossein BASIRI was born in 1959. His PhD is from The University of Nottingham, UK. He has several papers in the international journals and seminars. He is consultant of minister of Industries and Mines. He also is the chief of Iran Mining Engineering Organization. Besides he is assistant professor in the Tarbiat Modares University in Iran. His interests include decision support system, general management, and marketing.

Table 1. Pair-wise comparison scale (Saaty 1980)

Option                                                     Numerical
                                                            value(s)

Equal                                                          1
Marginally strong                                              3
Strong                                                         5
Very strong                                                    7
Extremely strong                                               9
Intermediate values to reflect fuzzy inputs                2, 4, 6, 8
Reflecting dominance of second alternative compared       reciprocals
  with the first

Table 2. SWOT analysis and strategic recommendations

            SWOT analysis                 Mining strategies

Internal    Strengths:                    A1. Improving the ability
            S1. High potential of ore     of exploitation and
            deposits,                     production: this strategy
            S2. Large mining              is obtained according to
            resources,                    S1, S2, O1, O2, O3.
            S3. Miscellaneous minerals    A2. Investment in
                                          exploration sector: this
            Weakness:                     strategy is resulted by
            W1. The lack of a             O3, O4, W1, W2.
            completed mining database     A3. Investing in the earth
            W2. Long period from          sciences (information,
            exploration to                technology, and labor
            manufacturing,                force): this strategy is
            W3. Low efficiency in         extracted from W1, W3, T1,
            mining activities             T3.

External    Opportunities:                A4. Making persuasive
            O1. Cheap Labor force,        policies to attract mining
            O2. Access to energy          investors and promotion of
            resource,                     R&D: this strategy is
            O3. The geopolitical          obtained through S1, S2,
            situation of Iran,            S3, T1, T2, T4.
            O4. Increasing demand for     A5. The privatization of
            raw materials                 mines and mineral
            Threats:                      industries: this strategy
            T1. Exporting raw             is resulted by O4, O3, W2,
            material,                     W3. A6. Revising the
            T2. Non-membership of Iran    mining law and cadastral
            in WTO,                       system: this strategy is
            T3. High risk involved,       extracted by T1, T2, T3,
            T4. The fluctuations of       S2.
            raw mineral prices

Table 3. Pairwise comparison of SWOT factors with assumption of
independence

SWOT factors      S       W       O       T     Relative importance
                                                  of SWOT factors

      S           1     2.37    3.76    3.22            0.49
      W         0.42      1     1.25    1.87            0.21
      O         0.26     0.8      1     0.69            0.13
      T         0.31    0.53    1.45      1             0.15
 GCR = 0.014

Table 4. The inner dependence matrix
with respect to "S"

    S          W       O     Relative importance
                                   weights

    W          1     1.63            0.62
    O        0.61      1             0.38
GCR = 0.0

Table 5. The inner dependence matrix
with respect to "W"

   W         S       T     Relative importance
                                 weights

   S         1     2.59            0.72
   T       0.38      1             0.28
GCR = 0

Table 6. The inner dependence matrix
with respect to "O"

   O         T       S     Relative importance
                                 weights
   T         1     0.29            0.77
   S       3.36      1             0.23
GCR = 0

Table 7. The inner dependence matrix
with respect to "T"

   T         W       O     Relative importance
                                 weights

   W         1     1.27            0.56
   O       0.61      1             0.44
GCR = 0

Table 8. Pairwise comparison matrices for SWOT sub-factors local
weights

      S          S1      S2      S3             Local weights

     S1         1.00    0.56    3.21               0.331309
     S2         1.79    1.00    4.86               0.55957
     S3         0.31    0.21    1.00               0.109121

GCR = 0.0017
      W          Wn1     Wn2     Wn3
     Wn1        1.00    0.43    0.34               0.158972
     Wn2        2.33    1.00    0.71               0.356581
     Wn3        2.94    1.41    1.00               0.484446

GCR = 0.0007
      O          O1      O2      O3      O4
     O1         1.00    1.12    0.39    0.58       0.176427
     O2         0.89    1.00    0.91    2.23       0.289132
     O3         2.56    1.10    1.00    0.97       0.304467
     O4         1.72    0.45    1.03    1.00       0.229975

 GCR = 0.073
      T          T1      T2      T3      T4
     T1         1.00    0.66    0.35    1.17       0.179075
     T2         1.52    1.00    0.47    0.87       0.204373
     T3         2.86    2.13    1.00    0.54       0.32839
     T4         0.85    1.15    1.85    1.00       0.288162

 GCR = 0.097

Table 9. A sample of decision matrix

      S1    S2    S3    Wn1   Wn2   Wn3

A1     4     8     3     2     3     2
A2     5     4     2     8     4     3
A3     4     4     5     8     3     4
A4     5     3     4     6     4     5
A5     5     5     5     4     3     1
A6     6     5     4     5     2     2

      O1    O2    O3    O4    T1    T2    T3    T4

A1     6     7     4     6     6     6     4     3
A2     3     4     5     5     5     7     9     2
A3     5     5     5     5     5     5     6     4
A4     6     4     6     6     6     4     5     3
A5     7     5     5     8     5     5     7     2
A6     5     5     3     5     7     1     6     5

Table 10. Important rating of each alternative

       S1      S2      S3      Wn1     Wn2     Wn3

A1    5.21    7.56    3.43    2.21    3.37    1.67
A2    6.11    5.23    2.18    8.14    4.56    3.32
A3    5.73    3.67    5.26    7.43    4.12    4.21
A4    5.09    3.16    3.78    6.57    5.23    6.42
A5    4.13     6.2    4.97    4.31    2.69    1.62
A6    5.89    5.14    4.29    4.74    2.34    2.31

       O1      O2      O3      O4      T1      T2      T3      T4

A1    6.13    7.79    5.24    6.56    6.46    4.93    4.21    3.19
A2    2.27    4.15    5.76    6.33    4.09    6.78    8.47    1.83
A3    4.16    4.77    4.33    5.89    6.24    4.43    6.31    4.15
A4    6.68    3.24    5.67    5.12    6.92    3.25    3.56    3.26
A5    8.06    5.86    5.23    8.47    5.13    5.14    7.49    2.16
A6    4.19    4.89    3.41    5.11    7.65    1.87    6.23    5.57

Table 11. the normalized decision matrix

        S1       S2       S3      Wn1      Wn2      Wn3       O1

A1    0.394    0.575    0.340    0.152    0.357    0.186    0.448
A2    0.462    0.398    0.216    0.561    0.483    0.370    0.166
A3    0.433    0.279    0.522    0.512    0.436    0.469    0.304
A4    0.385    0.240    0.375    0.453    0.554    0.715    0.488
A5    0.312    0.472    0.493    0.297    0.285    0.180    0.589
A6    0.445    0.391    0.426    0.327    0.248    0.257    0.306

        O2       O3       O4       T1       T2       T3       T4

A1    0.599    0.427    0.422    0.426    0.432    0.274    0.363
A2    0.319    0.469    0.407    0.270    0.594    0.550    0.208
A3    0.366    0.353    0.379    0.411    0.388    0.410    0.473
A4    0.249    0.462    0.329    0.456    0.285    0.231    0.371
A5    0.450    0.426    0.545    0.338    0.450    0.487    0.246
A6    0.376    0.278    0.329    0.504    0.164    0.405    0.634

Table 12. The weighted decision matrix

              S1      S2      S3      Wn1     Wn2     Wn3     O1

A1           0.05    0.12    0.01    0.01    0.04    0.03    0.02
A2           0.06    0.08    0.01    0.03    0.05    0.05    0.01
A3           0.05    0.06    0.02    0.02    0.05    0.07    0.01
A4           0.05    0.05    0.02    0.02    0.06    0.10    0.02
A5           0.04    0.10    0.02    0.01    0.03    0.03    0.02
A6           0.06    0.08    0.02    0.02    0.03    0.04    0.01
[A.sup.-]    0.04    0.05    0.01    0.05    0.11    0.15    0.01
[A.sup.*]    0.06    0.12    0.02    0.01    0.03    0.03    0.02

              O2      O3      O4      T1      T2      T3      T4

A1           0.03    0.02    0.02    0.01    0.01    0.01    0.01
A2           0.02    0.03    0.02    0.01    0.02    0.02    0.01
A3           0.02    0.02    0.02    0.01    0.01    0.02    0.02
A4           0.01    0.03    0.01    0.01    0.01    0.01    0.01
A5           0.02    0.02    0.02    0.01    0.01    0.02    0.01
A6           0.02    0.02    0.01    0.01    0.00    0.02    0.02
[A.sup.-]    0.01    0.02    0.01    0.02    0.03    0.04    0.04
[A.sup.*]    0.03    0.03    0.02    0.01    0.00    0.01    0.01

Table 13. Closeness coefficients and ranking of alternatives

Alternatives     [D.sup.*     [D.sup.-    [D.sub.j]    Rank
                 .sub.j]      .sub.j]

     A1          0.021737     0.167484     0.885123      1
     A2          0.06259      0.123179     0.663078      4
     A3          0.082423     0.110303     0.572331      5
     A4          0.113525     0.084794     0.427564      6
     A5          0.033317     0.161037     0.828573      2
     A6           0.0497      0.148577     0.749339      3