A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

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Huttagosol, P., & Cameron, R., 1992. A computer design of ultimate pit limit by using transportation algorithm, Proc. 23th Int. APCOM, pp.443-460. Huy, P. N. A., San, C. T. B., &Triantaphyllou, E., 2004. Solving integer programming problems using a genetic algorithm approach, Proceedings of ICEIC, Ha Noi, Viet Nam, 5 pages. Johnson, T. B., 1968. Optimum open pit mine production scheduling, PhD thesis, Operations research department, Uni. of California, Berkeley, C.A. Johnson, T. B., & Sharp, W. R., 1971.A 3-D dynamic programming method for optimal ultimate open pit design, U.S. Bureau of Mines, Report of investigation 7553. Kim, Y. C., 1979. Open pit limits analysis: technical overview, Computer methods for the 80's in the mineral industry, A. Weiss (ed.), AIME, New York, pp.297-303. Koenigsberg, E., 1982. The optimum contours of an open pit mine: an application of dynamic programming, Proc. 17th Int. APCOM, pp. 274- 287. Lerchs, H., & Grossmann, I., 1965. Optimum design of open-pit mines, Canadian Mining and Metallurgical Bulletin, 58, pp. 17–24. Meagher, C., Sabour, S. A. A., & Dimitrakopoulos, R., 2009. Pushback design of open pit mines under geological and market uncertainties, Proc. of Orebody Modelling and Strategic Mine Planning, Spectrum Series, pp.16 - 18. Meyer, M., 1969.Applying linear programming to the design of ultimate pit limits, Management Science, vol. 16, pp.B121- B135. Mol, O., &Gillies,S.,1984. Cutoff grade determination of mines producing direct shipping iron ore, Proc. of Australasian Institute of Mining and Metallurgy, No. 289, November/December 1984, pp.283-287. Muir, D. C. W., 2004. Pseudoflow, new life for Lerchs-Grossmann pit optimisation, Proc. of Orebody Modelling and Strategic Mine Planning, pp.113-120. Newman, A. M., Rubio, E., & Caro, R., Weintraub, A., &Eurek, K., 2010. A review of operations research in mine planning, Interfaces, 40(3), pp.222-245. Olivetti, F. F., 2003. Solving integer programming problems using genetic algorithms, website http://ai-repot.com/Articles/48/Programming.html Osanloo, M., Rahmanpour, M., & Sadri, A. 2010. Ultimate pit limit of iron ore mines using maximum flow algorithms, Proc. MPES 2010, FREMANTLE: AusIMM, pp.81-87. Rahmanpour, M., & Osanloo, M., 2012. Pit limit determination considering blending requirements. Proc. of 21st International Symposium on Mine Planning & Equipment Selection (MPES), New Delhi, India, pp.564-572. Picard, J. C., 1976. Maximal closure of a graph and application to combinatorial problems, Management Science, 22, pp.1268-1272. Qin, A. K., Huang, V. L., &Suganthan, P. N., 2009, Differential evolution algorithm with strategy adaption for global numerical optimization, IEEE transactions on evolutionary computation, Vol. 13, No. 6, pp. 398-417. Srinivasan, S., & Whittle, D., 1996. Combined pit and blend optimization, SME Annual Meeting, Phoenix, Arizona, March 11-14, Preprint 96-69, 5 pages. Storn, R., & Price, K., 1997. Differential evolution- optimization over continuous spaces, J. Global Optimiz., vol. 11, pp.341–359. Underwood, R., &Tolwinski, B., 1998. A mathematical programming viewpoint for solving the ultimate pit problem, European J. of Operational Research, 107, pp.96-107. Whittle, D., &Bozorgebrahimi, A., 2004. Hybrid Pits - linking conditional simulation and Lerchs- Grossmann through set theory, Proc. of Orebody Modelling and Strategic Mine Planning, Spectrum Series Vol. 14, pp.323-328. Yegulalp, T. M., & Arias, A. J., 1992.A fast algorithm to solve the ultimate pit limit problem, Proc. 23th Int. APCOM, pp.391-397. Zhao, Y., & Kim, Y. C., 1992.A new optimum pit limit design algorithm, Proc. 23th Int. APCOM, pp.423-434. 128
23 rd Selecting the Suitable Haulage System Using HPVS Method, Considering Sustainable Development Concepts M. Jamshidi, M. Osanloo Department of Mining and Metallurgy, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran ABSTRACT Transportation costs have always been a significant part of large open pit mining capital and operating costs. It counts about 40 up to 60 percent of the total operating cost in open pit mining. The main goal of design a haulage system is an implementation of a plan that meets the required production requirements, operates at competitive cost, and complies with environmental regulations. Selecting the suitable haulage system can be considered as a MADM problem because the effective parameter are various, with different importance. In this study, the HPVS method that uses a DEA model to produce weights associated with each ranking place was applied to select the suitable haulage system considering sustainable development (SD) concept. This approach was tested in Sarcheshmeh copper mine of Iran. The result of this model shows that the combination of truck-shovel and in-pit crusher is as the most appropriate haulage system in Sarcheshmeh copper mine. 1 INTRODUCTION One of the main proportions of operating cost of open pit mining is related to haulage capital and operating costs. Currently, truck with 360-ton capacity is running in some open pit and some researcher give indication to the employment of 420-ton capacity truck (Jacek, Zaplicki, 2009). The capital cost of truck with capacity around 300 ton reach above 2.5 million US $. Therefore, equipment selection is one of the most important aspects of open pit mine planning and design. The purpose of equipment selection is to select optimum size and number of equipment with minimum cost (Osanloo, 2007). The selection of equipment for mining applications is not a well-defined process, because it involves the interaction of several subjective factors or criteria, decision on mine equipment selection in different mines are often complicated and may even embody contradictions. Different types of models have been developed to selecting the suitable mining equipment. Expert system as decision aid in surface mine equipment selection was applied by Bandopadhyay, et al. (Bandopadhyay et al 1987) and Denby, Schofield (Denby, Schofield 1990). Hrebar (Hrebar 1990), Sevim and Sharma used net present value analysis for selection of a dragline and surface transportation system (Sevim, Sharma 1991). Cebesoy used linear break-even model (Cebesoy 1997). Models for equipment selection and evaluation described by Celebi were aimed at selection of the equipment fleet based on minimizing the unit stripping cost and maximizing production (Celebi 1998). Hall et al. illustrated how reliability analysis can provide mine management with quantitative information of value for decision making about surface mining equipment (Hall et al. 129
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A OPEN PIT MINING AÇIK OCAK MADENCİLİĞİ

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