Organization: Society for Mining, Metallurgy & Exploration

Pages: 4

Publication Date:
Jan 1, 2009

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Abstract

Underground mining method selection (UMMS) is one of the most important issues in mining engineering. Underground mining method should be primarily selected to make use of underground resources optimally. It is nearly impossible to change the mining method once selected by the reasons of rising costs and mining losses. Because of these reasons, UMMS process is extremely important in mine designs and economics. The strategy of underground mining method selection has been studied by different researchers in the literature. Boshkov and Wright (1973) proposed a classification system; Morrison (1976) suggested a selection chart; Laubscher (1981) proposed a selection methodology; Nicholas (1981) presented a classification system; Hartman (1987) developed a selection chart and Miller-Tait et al. (1995) modified the Nicholas? system and developed the UBC mining method selection process. In reality, UMMS is one of the important Multiple Attribute Decision Making (MADM) problems and mining engineers have always some difficulties in making the right decision in the multiple criteria environment. To make a right decision on UMMS, all known criteria related to the mining method selection should be analyzed and the problem solved according to suitable decision making techniques. A review of the literature reveals that decision making techniques have been used for UMMS applications. Kesimal and Bascetin (2002), Bitarafan and Ataei (2004), Bascetin et al. (2006), Alpay and Yavuz (2007), Karadogan et al. (2008) and Samimi Namin et al. (2008) used decision making techniques in UMMS applications. In this paper, two different MADM methods: Analytic Hierarchy Process (AHP) and Fuzzy Multiple Attribute Decision Making (FMADM) are applied to select the optimal underground mining method for a chromite mine and final solutions are then compared. THE AHP METHODOLOGY The AHP method developed by Saaty gives an opportunity to represent the interaction of multiple factors in complex unstructured situations (Triantaphyllou, 2000). The method is based on the pair-wise comparison of components with respect to attributes and alternatives. A pair-wise comparison matrix n×n is constructed, where n is the number of elements to be compared. The method is applied for the hierarchy problem structuring. The problem is divided in to three levels: problem statement, object identification to solve the problem and selection of evaluation criteria for each object. After the hierarchy structuring, the pair-wise comparison matrix is constructed for each level where a nominal discrete scale from 1 to 9 is used for the evaluation as shown in Table 1 (Saaty, 1980; Saaty, 2000). The next step is to find the relative priorities of criteria or alternatives implied by this comparison. The relative priorities are worked out using the theory of eigenvector. For example, if the pair comparison matrix is A, then, [Amax=××- [1] ] To calculate the eigenvalue ??max? and eigenvector w=(w1, w2,..., wn), weights can be estimated as relative priorities of criteria or alternatives. [ ] Since the comparison is based on the subjective evaluation, a consistency ratio is required to ensure the selection accuracy. The Consistency Index (CI) of the comparison matrix is computed as follows: [CImax--= [2] ] where ?max is maximal or principal eigenvalue, and n is the matrix size. The consistency Ratio (CR) is calculated as: [CR= [3 ] where ?RI? Random Consistency Index. Random consistency indices are given in Table 2. [ ] Table 2. The consistency indices of randomly generated reciprocal matrices As a general rule, a consistency ratio of ?0.10? or less is considered acceptable. In practice, however, consistency ratios exceeding ?0.10? occur frequently. THE FMADM METHODOLOGY The FMADM methods have been developed due to the lack of precision in assessing the relative importance of attributes and the |

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