A Fuzzy AHP Approach to Select the Proper Roadheader in Tabas Coal Mine Project of Iran.

Year 2014, , 141 - 168, 01.04.2014

Arash Ebrahımabadı

https://doi.org/10.17824/huyuamd.81938

Cited By: 1

Abstract

Machinery equipment selection, particularly mechanical excavators in mechanized mining operations, is one of the most important issues through a mine project planning and design, and has a remarkable effect on speed and cost of excavating operation. Therefore, it is an essential matter and needs to be concerned and managed appropriately. Alike other mechanized projects, mechanized coal mining is very machinery-intensive so that appropriate equipment selection plays a key role in project’s success and productivity. In this respect, it is crucial to consider the basic parameters such as geological and geotechnical properties of ore deposit, its surrounding strata, economic and technical parameters, etc through the selection process; hence, choosing the major equipment and mechanical miners such as roadheaders in mechanized coal mining is a multi-criteria decision making problem. A multi-criteria decision making method is used to rank available roadheaders based on a set of criteria, ultimately leading to suggest the high-ranked one as the best option.This paper presents an evaluation model based on Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach to select the proper roadheading machine in Tabas coal mine project; the largest and the only fully mechanized coal mine in Iran. This method assists mine designers and decision makers in the process of roadheader selection under fuzzy environment where the vagueness and uncertainty are taken into account with linguistic variable parameterized by triangular fuzzy numbers. The broad issue includes three possible roadheading machines and five criteria to evaluate them. The suggested method applied to the mine and the most appropriate roadheader, among three candidate roadheaders, has been ranked and selected as DOSCO MD1100 roadheader with the highest weight of 0.435. The weights of other options namely KOPEYSK KP21 and WIRTH T2.11 found as 0.323 and 0.242, respectively

Keywords

Multi-Criteria Decision Making; Fuzzy Analytic Hierarchy Process; Roadheader Selection; Tabas Coal

İran Tabas Kömür Madeni Projesinde Uygun Tünel Açma Makinası Seçimi için Bulanık AHP Yaklaşımı.

Abstract

Özellikle mekanize madencilik işletmelerinde kullanılan mekanik kazıcılarda olduğu gibi makina techizat seçimi,bir maden projesi planlaması ve dizaynındaki en önemli konudur ve kazma işleminin hızı ve maliyeti üzerinde belirgin etkisi bulunmaktadır. Bu nedenle, önemli bir konu olup uygun şekilde ilgilenilmesi ve işletilmesi gerekmektedir.Tıpkı diğer mekanize projelerdeki gibi, mekanize kömür madenciliği makina yoğunluğunun çok fazla olduğu bir alan olup, uygun ekipman seçimi projenin başarısında ve üretimde anahtar rol oynar.Bu bağlamda, maden yatağının jeolojik ve jeoteknik temel parametreleri, çevreleyen seviyelerin özellikleri ile ekonomik ve teknik parametrelerin hesaba katılmasıçok önemlidir. Dolayısıyla, mekanize kömür madenciliğindeki tünel açma makinaları gibi ana ekipman seçimi, mekanize kömür madenciliğinde çok-kriterli karar almayı gerektiren problem oluşturur. Çok-kriterli karar alma yöntemi bir dizi kriter baz alınarak en çok opsiyonda en yüksek dereceyi alabilen tünel açma makinalarını derecelendirmekte kullanılır. Bu makale, İran’ın en büyük ve tek tam mekanize olarak çalışan Tabas kömür madeni projesine uygun tünel açma makinasını Bulanık Analitik Hiyerarşi İşlemi (Fuzzy AHP) yöntemine dayalı değerlendirme modeli sunmaktadır.Bu yöntem, tünel açma makinası seçiminde maden ocağı tasarımcılarına ve karar mercilerine belirsiz koşulların olduğu durumda destek olacaktır. Piyasada yaygın olan üç olası tünel açma makinası ile değerlendirme aşamasında kullanılan beş kriter çalışma kapsamında ele alınmıştır.Önerilen yöntem madene uygulanmış ve üç aday arasından en uygun tünel açma makinası olan, 0.435 ağırlıkla DOSCO MD1100 seçilmiştir. Diğer seçeneklerden olan KOPEYSK KP21 ve WIRTH T2.11 sırasıyla 0.323 ve 0.242 ağırlık notu almıştır

Keywords

Çok-kriterli karar verme, bulanık analitik hiyerarşi işlemi, tünel kazma makinası seçimi, Tabas kömür madeni projesi

References

• Acaroglu, O., Ergin, H., and Eskikaya, S., 2006. Analytical hierarchy process for selecti- on of roadheaders. Journal of the South African Institute of Mining and Metal- lurgy (SAIMM), 106, 569-575.
• AkerSolutions, 2014. www.AkerSolutions.com
• Alpay, S., and Yavuz, M., 2009. Underground mining method selection by decision making tools. Tunneling and Underg- round Space Technology, 24, 173-184.
• Ataei, M., Jamshidi, M., Sereshki, F. and Jalali, S. M. E., 2008. Mining method selection by AHP approach. Journal of the South African Institute of Mining and Metal- lurgy, 741-749.
• Aydogan, E. K., 2011. Performance measure- ment model for Turkish aviation firms using the rough-AHP and TOPSIS met- hods under fuzzy environment. Expert Systems with Applications, 38, 3992- 3998.
• Bitarafan, M. R. and Ataei, M., 2004. Mining method selection by multiple criteria decision making tools. Journal of the South African Institute of Mining and Metallurgy, 104 (9), 493-498.
• Boender, C.G.E., de grann, J. G., and Lootsma, F. A., 1989. Multicriteria decision analy- sis with fuzzy pair-wise comparison. Fuzzy Sets and Systems, 29 (2), 133- 143.
• Bojadziev, G., and Bojadziev, M., 1998. Fuzzy Sets and Fuzzy Logic Applications, World Scientific, Singapore, p. 300.
• Buckley, J., 1985. Fuzzy Hierarchical Analysis. Fuzzy Sets Systems, 17, 233-247.
• Chang, D. Y., 1992. Extent analysis and synthe- tic decision, Optimisation Techniques and Applications, 1, World Scientific, Singapore, 352 p.
• Chang, D. Y., 1996. Applications of the extent analysis method on Fuzzy AHP. Euro- pean Journal of Operation Research, 95, 649-655.
• Deng, H., 1999. Multicriteria analysis with Fuzzy pair-wise comparison. International Jo- urnal of Approximate Reason, 21, 215- 231.
• DOSCO Overseas Engineering Ltd, 2008. Ne- wark Nottinghamshire, England, www. dosco.co.uk.
• Ebrahimabadi, A., Goshtasbi, K., Shahriar, K., and Cheraghi Seifabad, M., 2011a. A model to predict the performance of roadheaders based on rock mass britt- leness index. Journal of the South Afri- can Institute of Mining and Metallurgy (SAIMM), 111(5), 355-364.
• Ebrahimabadi, A., Goshtasbi, K., Shahriar, K., and Cheraghi Seifabad, M., 2011b. Pre- dictive models for roadheaders’ cutting performance in coal measure rocks.
• Yerbilimleri, 32(2), 89-104.
• Ebrahimabadi, A., Goshtasbi, K., Shahriar, K., and Cheraghi Seifabad, M., 2012. A universal model to predict roadheaders’ cutting performance. Archives of Mi- ning Sciences, 57(4), 1015-1026.
• Ertugrul, I., and Tus, A., 2007. Interactive Fuzzy linear programming and an application sample at a mine firm. Fuzzy Optimiza- tion Decision Making, 6, 29-49.
• Ertugrul, I., 2011. Fuzzy group decision making for the selection of facility location. Gro- up Decision and Negotiation, 20(6),725- 740.
• Feizizadeh, B., Shadman Roodposhti, M., Jan- kowski, P., and Blaschke, T., 2014. A GIS-based extended fuzzy multi-crite- ria evaluation for landslide susceptivity maping. Computer and Geosciences, 73,208-221.
• Hamrin, H., 1986. Guide to underground mining methods and applications, Atlas Cop- co., Stockholm.
• Hartman, H. L. 1992. Selection procedure, SME Mining Engineering Handbook, Ed., Hartman, H. L., 2nd ed., Society for Mi- ning Engineering, Metallurgy and Exp- loration, Inc, chap. 23.4,2090-2106.
• Kahraman, C., Cebeci, U., and Ulukan, Z., 2003. Multi-criteria supplier selection using Fuzzy AHP. Logistic Information Mana- gement, 16(6),382-394.
• Kahraman, C., Cebeci, U., and Ruan, D., 2004. Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey. International of Pro- duction Economics, 87,171-184.
• Kahraman, C., 2008. Multi-criteria decision ma- king methods and fuzzy sets. Fuzzy Multi-Criteri Decision Making, Springer Science& Business Media, LLC, 16,1- 18.
• Klir, G. J., and Yuan, B., 1995. Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall.
• Kopeysk Machine-building Plant, 2014. www. kopemash.ru
• Oguzitimur, S., 2011. Why fuzzy analytic hie- rarchy process approach for transport problems? In: European Regional Sci- ence Association ERSA Conference Papers, Vienna, Austria, ersa11, 438.
• Rostami, J., Ozdemir, L., and Neil, D.M., 1994. Performance prediction: a key issue in mechanical hard rock mining. Mining Engineering, 1263-1267.
• Saaty, T. L., 1980. The Analytic Hierarchy Pro- cess, McGraw-Hill, New York.
• Tutmez, B., and Tercan, A. E., 2007. Spatial es- timation of some mechanical properti- es of rocks by fuzzy modelling. Com- puters and Geotechnics, 34(1),10-18. DOI: 10.1016/j.compgeo.2006.09.005.
• Tutmez, B., and Kaymak, U., 2008. Fuzzy opti- mization of slab production from mec- hanical stone properties. Structural and Multidisciplinary Optimization, 37(1),71- 76.
• Vahdani, B., and Hadipour, H., 2010. Extention of the ELECTRE method based on in- terval-valued fuzzy set. Soft Computing, http://dx.doi.org/10.1007/s00500-010- 0563-5.
• Vaida, O. S., and Kumar, S., 2006. Analytic Hi- erarchy Process: an overview of appli- cations. European Journal of Operation Research, 169,1-29.
• Van Laarhoven, P. J. M., and Pedrcyz, W., 1983. A Fuzzy extension of Saaty’s priority theory. Fuzzy Sets Systems, 11,229- 241.
• Yazdani-Chamzini, A., and Yakhchali, S. H., 2012. Tunnel Boring Machine (TBM) se- lection using fuzzy multicriteria decision making methods. Tunnelling and Un- derground Space Technology, 30,194- 204.
• Zadeh, L. A., 1965. Fuzzy sets. Information Control, 8,338-353.