SciELO - Scientific Electronic Library Online

vol.108 número7The 2007 South African Mineral and Petroleum Resources Draft Royalty Bill: An independent analysisGround support strategies to control large deformations in mining excavations índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados



Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Em processo de indexaçãoSimilares em Google


Journal of the Southern African Institute of Mining and Metallurgy

versão On-line ISSN 2411-9717
versão impressa ISSN 0038-223X

J. S. Afr. Inst. Min. Metall. vol.108 no.7 Johannesburg Jul. 2008




A new model for mining method selection of mineral deposit based on fuzzy decision making



F. Samimi Namin; K. Shahriar; M. Ataee-pour; H. Dehghani

Department of Mining, Metallurgical and Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran




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



“Full text available only in PDF format”




1. KARADOGAN, A., KAHRIMAN, A., and OZER, U. Application of fuzzy set theory in the selection of underground mining method. The Journal of the South African Institute of Mining and Metallurgy, 2008. vol. 108. pp. 73-79.         [ Links ]

2. BITARAFAN, M.R. and ATAEI, M. Mining method selection by multiple criteria decision making tools. The Journal of the South African Institute of Mining and Metallurgy, October 2004. pp. 493-498.         [ Links ]

3. PEELE, R. and CHURCH, J. Mining Engineering Handbook, John Wiley and Sons, INC. 1941. vol. 1.         [ Links ]

4. BOSHKOV, S., and WRIGHT, F. Basic and Parametric Criteria in the Selection, Design and Development of Underground Mining System. SME Mining Engineering Handbook. Cummins and Given. SME. New York. 1973. vol. 1. pp. 12.2-12.13        [ Links ]

5. MORRISON, R.G.K. AQ Philosophy of Ground Control. McGill University. Montreal. Canada. 1976. pp. 125-159.         [ Links ]

6. HARTMAN, H.L. Introductory mining engineering. John Wiley and sons, Inc, Second edition. 2002.         [ Links ]

7. AGOSHKOV, M., BORISOV, S., and BOYARSKY, V. Classification of Ore Deposit Mining Systems. Mining of Ores and Non-Metalic Minerals. Union of Soviet Socialist Republics. 1988. pp. 59-62.         [ Links ]

8. LABSCHER, D. Selection of Mass Underground Mining Methods. Design and Operation of Caving and Sublevel Stoping Mines. 1981, New York, AIME, Chapter 3.         [ Links ]

9. NICHOLAS, D. and MARK, J. Feasibility Study-Selection of a Mining Method Integrating Rock Mechanics and Mine Planning, 5th Rapid Excavation and Tunneling Conference. 1981. San Francisco. vol. 2. pp. 1018-1031.         [ Links ]

10. NICHOLAS, D.E. Selection Procedure. Mining Engineering Handbook. Hartman, H. SME. New York, 1993. pp. 2090-2105.         [ Links ]

11. MILLER, L., PAKALNIS, R., and POULIN, R. UBC Mining Method Selection. International symposium on mine planning and equipment selection. Singh. 1995.         [ Links ]

12. HARTMAN, H.L. Introductory mining engineering. John Wiley and sons, Inc, Second edition. 2002.         [ Links ]

13. YIMING, W., YING, F., and WEIXUAN, X. An Integrated Methodology for Decision Making of Mining Method Selection. Manufacturing Technology and Management. China. 2003        [ Links ]

14. YIMING, W., YING, F., and WEIXUAN, X. Multiple Objective-integrated methodology of Global Optimum Decision-Making on Mineral Exploitation. Computer & Industrial Engineering, vol. 46, 2004. pp. 363-372.         [ Links ]

15. KESIMAL, A. and BASCETIN, A. Application of Fuzzy Multiple Attribute Decision Making in Mining Operations. Mineral Resources Engineering. 2002, vol. 11, pp. 59-72.         [ Links ]

16. MIRANDA, C. and ALMEIDA. Mining Methods Selection Based on Multicriteria Models. Application of Computes and operation research in the mineral industry. London. 2005.         [ Links ]

17. SAMIMI NAMIN, F., SHAHRIAR, K., and KARIMI NASAB, S. Fuzzy Decision Making for Mining Method Selection in Third Anomaly Gol-E-Gohar Deposit. 18th International mining congress and exhibition of Turkey, I MCET. 2003.         [ Links ]

18. SHAHRIAR, K., SHARIATI, V., and SAMIMI NAMIN, F. Geomechanical Characteristics Study of Deposit in Underground Mining Method Selection Process. 11th ISRM Conferences, 2007. Portugal.         [ Links ]

19. BASCETIN, A., OZTAS, O., and KANLI, A.I. Mining method selection by multiple criteria decision making tools. The Journal of the South African Institute of Mining and Metallurgy, 2006. vol. 106. pp. 63-69.         [ Links ]

20. YAGER, R.R. A new methodology for ordinal multi objective decisions objectives based on Fuzzy sets, Decision Science, 1978, vol. 12. pp. 589-600.         [ Links ]

21. BELLMAN, R.E. and ZADEH, L.A. Decision Making In a Fuzzy Environment, Management Science, vol. 17, 1970. pp. 141-164.         [ Links ]

22. SAATY, T.L. Decision-making for Leaders, RWS Publication, USA. 1990.         [ Links ]

23. MACHARIS, C., SPRINGAEL, J., DE BRUCKER, K., and VERBEKE, A. PROMETHEE and AHP: The design of operational synergies in multi-criteria analysis, Strengthening PROMETHEE with ideas of AHP. European Journal of operational Research, vol. 153, 2004. pp. 307-317.         [ Links ]

24. SHYUR, H.J. Cost Evaluation Using Modified TOPSIS and ANP. Applied mathematics and computation, vol. 177, 2006. pp. 251-259.         [ Links ]

25. SHYUR, H.J. and SHIH, H.S. A hybrid MCDM model for Strategic vendor selection. Mathematical and Computer Modeling, vol. 44, 2006. pp. 749-761.         [ Links ]

26. KIM, G., PARK, C.S., and YOON, K.P. Identifying Investment Opportunities for Advanced Manufacturing Systems with Comparative-Integrated Performance Measurement, International Journal of Production Economics, vol. 50, 1997. pp. 23-33.         [ Links ]

27. SHIH, H.S., SHYUR, H.J., and LEE, E.S. An extension of TOPSIS for Group Decision Making. Mathematical and Computer Modeling, vol. 45, 2007. pp. 801-813.         [ Links ]

28. ABO-SINNA, M.A. and AMER, A.H. Extensions of for multi-objective largescale nonlinear programming problems. Applied Mathematics and Computation, vol. 162, 2005. pp. 243-256.         [ Links ]

29. AGRAWAL, V.P., KOHLI, V., and GUPTA, S. Computer aided robot selection: The multiple attribute decision making approach. International Journal of Production Research, vol. 29, 1991. pp. 1629-1644.         [ Links ]

30. CHENG, S., CHAN, C.W., and HUANG, G.H. An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Engineering Applications of Artificial Intelligence, vol. 16, 2003. pp. 543-554.         [ Links ]

31. DENG, H., YEH, C.H., and WILLIS, R.J. Inter-company comparison using modified with objective weights. Computers and Operations Research, vol. 27, 2000. pp. 963-973.         [ Links ]

32. FENG, C.M. and WANG, R.T. Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, vol. 6, 2000. pp. 133-142.         [ Links ]

33. FENG, C.M. and WANG, R.T. Considering the financial ratios on the performance evaluation of highway bus industry. Transport Reviews, vol. 21, 2001. pp. 449-467.         [ Links ]

34. HWANG, C.L. and YOON, K. Multiple attribute decision making: Methods and applications. Berlin: Springer. 1981        [ Links ]

35. JEE, D.H. AND KANG, K.J. A method for optimal material selection aided with decision making theory. Materials and Design, vol. 21, 2000. pp. 199-206.         [ Links ]

36. KIM, G., PARK, C.S., and YOON, K.P. Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, vol. 50, 1997. pp. 23-33.         [ Links ]

37. LAI, Y.J., LIU, T.Y., and HWANG, C.L. For MODM. European Journal of Operational Research, vol. 76, 1994. pp. 486-500.         [ Links ]

38. LIAO, H.C. Using PCR- to optimize Taguchi's multi-response problem. The International Journal of Advanced Manufacturing Technology, vol. 22, 2003. pp. 649-655.         [ Links ]

39. OLSON, D.L. Comparison of weights in models. Mathematical and Computer Modelling, vol. 40, 2004. pp. 721-727.         [ Links ]

40. OPRICOVIC, S., and TZENG, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR. European Journal of Operational Research, vol. 156, 2004. pp. 445-455.         [ Links ]

41. PARKAN, C. and WU, M.L. On the equivalence of operational performance measurement and multiple attribute decision making. International Journal of Production Research, vol. 35, 1997. pp. 2963-2988.         [ Links ]

42. PARKAN, C. AND WU, M.L. Decision-making and performance measurement models with applications to robot selection. Computers and Industrial Engineering, vol. 36, 1999. pp. 503-523.         [ Links ]

43. TONG, L.I. and SU, C.T. Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Quality and Reliability Engineering International, vol. 13, 1997. pp. 25-34.         [ Links ]

44. TZENG, G.H., LIN, C.W., and OPRICOVIC, S. Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy, vol. 33, 2005. pp. 1373-1383.         [ Links ]

45. ZANAKIS, S.H., SOLOMON, A., WISHART, N., and DUBLISH, S. Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, vol. 107, 1998. pp. 507-529.         [ Links ]

46. CHEN, C.T. Extension of the for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 2000. pp. 114, 1-9.         [ Links ]

47. CHEN, M.F. and TZENG, G.H. Combining grey relation and concepts for selecting an expatriate host country. Mathematical and Computer Modelling, vol. 40, 2004. pp. 1473-1490.         [ Links ]

48. Chu, T. C. Facility location selection using fuzzy TOPSIS under group decisions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002. Vol. 10, Pp. 687-701.         [ Links ]

49. CHU, T.C. Selecting plant location via a fuzzy TOPSIS approach. The International Journal of Advanced Manufacturing Technology, 2002, vol. 20, pp. 859-864.         [ Links ]

50. CHU, T.C. and LIN, Y.C. A fuzzy TOPSIS method for robot selection. The International Journal of Advanced Manufacturing Technology, vol. 21, 2003. pp. 284-290.         [ Links ]

51. KAUFMANN, A. and GUPTA, M.M. Introduction to fuzzy arithmetic: Theory and applications. New York: VanNostrand-Reinhold. 1991        [ Links ]

52. TRIANTAPHYLLOU, E. and LIN, C.T. Development and evaluation of five fuzzy multiattribute decision-making methods. International Journal of Approximate Reasoning, vol. 14, 1996. pp. 281-310.         [ Links ]

53. YING-MING WANG and TAHA, M.S. Elhag. Fuzzy method based on alpha level sets with an application to bridge risk assessment. Expert systems with applications, 2005. pp. 1-11.         [ Links ]

54. ZADEH, L.A. Fuzzy sets. Information control. vol. 8, 1965. pp. 338-353.         [ Links ]

55. MEAMARIANI, A. FDM software (Fuzzy Decision Meaking). Tarbiat Modares University. Tehran, Iran. 2003.         [ Links ]

56. SHAHRIAR, K., SAMIMI, F., and DEHGHAN, H. Mining Method Selection of Chahar Gonbad Deposit Based on Fuzzy Decision Making (FDM), 2007, 20th International Mining Congress of Turkey (IMCET), pp. 143-150.         [ Links ]

57. SAMIMI NAMIN, F. Underground mining method selection based on decision theory. Ph.D. Thesis. Faculty of Mining and Metallurgical Engineering. Amirkabir University of Technology. Tehran. Iran.         [ Links ]

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons