Journal of the Southern African Institute of Mining and Metallurgy
On-line version ISSN 2411-9717
Print version ISSN 0038-223X
SAMIMI NAMIN, F.; SHAHRIAR, K.; ATAEE-POUR, M. and DEHGHANI, H.. A new model for mining method selection of mineral deposit based on fuzzy decision making. J. S. Afr. Inst. Min. Metall. [online]. 2008, vol.108, n.7, pp.385-395. ISSN 2411-9717.
One of the main tasks in exploitation of mines is to select a method suitable for the deposits, specific features including geometric, geomechanical and geological features. The purpose of selecting a mining method at this stage is to choose one or more method(s) having the most coordination with the deposit's conditions and external factors, including the allocated budget and local technology for detailed feasibility studies. Several methods have been developed in the past to evaluate suitable mining methods for an ore deposit, based on its physical characteristics. These approaches can be classified into three groups: (1) profile and checklist methods, (2) numerical ranking (scoring) methods, and (3) decision-making models. Most of these methods have shortcomings. Considering the fact that most of the specifications of mineral deposits, such as dip and depth, are linguistic variables, absoluteness of the explaining parameters by previous methods can be mentioned as the most important disadvantage of these methods. This paper discusses the Fuzzy technique for order performance by similarity to ideal solution (Fuzzy TOPSIS) to determine the mining method. The fuzzy decision making (FDM) software tool is employed to develop a Fuzzy TOPSIS based model. Application of this model with various values (crisp, linguistic and fuzzy) of the deposit eliminated the existing disadvantages of other methods. Two empirical illustrations demonstrate the effectiveness and feasibility of the evaluation procedure. These show that the proposed model performs better than its alternatives.
Keywords : mining method selection; fuzzy TOPSIS; decision making; mining engineering; linguistic variables.