Ranking the sawability of ornamental and building stones using different MCDM methods
AbstractStone cutting has got an important role and place in the development of stone technology as the first and most important engineering process of stone production. The selection of proper stone with high sawability is very impressive on the quality and time and energy consumption. Therefore, deciding on the selection of stone with several characteristics, often opposing to each other, is difficult and complicated. Multi-criteria decision making (MCDM) methods are suggested to solve such problems. In this research, ranking of 12 cases of decorative stones of two carbonate and granite types is conducted. Firstly, the initial ranking of cases was conducted using MCDM methods including TOPSIS, ELECTRE II, PROMETHEE II, VIKOR, ORESTE and COPRAS. Also, Spearman’s ranking correlation coefficient indicated that most of the foregoing six methods have high correlation, and there exist little differences between them. Secondly, regarding the differences between the rankings of the methods, two MCDM techniques of REGIME and QUALIFLEX were employed in order to achieve a single consistent ranking. The results showed that these two methods lead to identical coherent ranking.
Aghajani Bazazi, A., Osanloo, M. & Karimi, B. 2011. Deriving preference order of open pit mines equipment through MADM methods: application of modified VIKOR method. Expert Systems with Applications. 38(3): 2550-2556.
Alinezhad, A. & Esfandiari, N. 2012. Sensitivity Analysis in the QUALIFLEX and VIKOR Methods. Journal of Optimization in Industrial Engineering. 10:29-34.
Alptekin, N. 2013. Integration of SWOT Analysis and TOPSIS method in strategic decision making process. The Macrotheme Review. 2(7).
Arab Halvaie, A. 2009. Application of the police command prompt decisions. Human Bimonthly Police. 6(23): 21-43.
Asgari Ghashtrodkhani, O., Asgarian Abyane, H. & Vahidi, B. 2014. A new approach for planning transition development based on VIKOR method. International Electronics Conference – Tehran, Iran.
Atai, M. 2008. Choosing the perfect place to establish alumina plant - cement using Electre. International Journal of Engineering at the University of Science and Technology of Iran, Special issue of Materials Engineering, Mining Engineering, Civil Engineering. 19(9): 55-63.
Ataei, M., Sereshki, F., Jamshidi, M. & Jalali, S. M. E. 2008. Suitable mining method for the Golbini No.8 deposit in Jajarm (Iran) by using TOPSIS method. Mining Technology: Transactions of the Institute of Mining & Metallurgy, Section A. 117(1): 1-5.
Brans, J.P. 1982. Lingenierie de la decision. Elaboration dinstruments daide a la decision. Methode PROMETHEE. In: Laide a la Decision: Nature. Instrument set Perspectives Davenir, Nadeau, R., Landry, M. (Eds.). Presses de Universite Laval, Quebec, Canada: 183-214.
Brans, J.P. & Vincke, Ph. 1985. A preference ranking organization method: the PROMETHEE method. Management Science. 31: 647-656.
Brans, J.P. & Mareschal, B. 1994. The PROMCALC & GAIA decision support system for multicriteria decision aid. Decision Support Systems. 12 (4-5): 297-310.
Buttner, A. 1974. Diamond tools and stone. Industrial Diamond Review. 34: 89-93.
Ceylanoglu, A. & Gorgulu, K. 1997. The performance measurement results of stone cutting machines and their relations with some material properties. Proceedings of the Sixth International Symposium on Mine Planning and Equipment Selection. Rotterdam, Balkema: 393–398.
Chakraborty, R., Ray, A. & Dan, P., K. 2013. Multi criteria decision making methods for location selection of distribution centers. International Journal of Industrial Engineering Computations. 4(4): 491-504.
Chatterjee, P. & Chakraborty, SH. 2014. Flexible manufacturing system selection using preference ranking methods: A comparative study. International Journal of Industrial Engineering Computations. 5: 315-338.
Chandra Das, M., Sarkar, B. & Ray, S. 2012. A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology. Socio-Economic Planning Sciences. 46: 230-241.
Ching-Lai, H. and Yoon, K. “Multiple Attribute Decision Making: Methods and Applications. Springer, Berlin Heidelberg, (1981).
Delgado, N.S., Rodriguez, R., Rio, A., Sarria, I.D., Calleja, L. & Argandona, V.G.R. 2005. The influence of microhardness on the sawability of Pink Porrino granite (Spain). International Journal of Rock Mechanics & Mining Sciences. 42(1): 161–166.
Ertingshausen, W. 1985. Wear processes in sawing hard stone. Industrial Diamond Review. 5: 254–258.
Eyuboglu, A. S., Ozcelik, Y., Kulaksiz, S. & Engin, I. C. 2003. Statistical and microscopic investigation of disc segment wear related to sawing Ankara andesites. International Journal of Rock Mechanics & Mining Sciences. 40: 405-414.
Faraji Sabokbar, H., Rezvani, M.R., Behnam morshedi, H. & Rosta, H. 2015. Space-Rating of Fars Province based upon services and tourist facilities. Human Geographical Research. 46(3): 561-586.
Jennings, M. & Wright, D.N. 1989. Guidelines for sawing stone. Industrial Diamond Review. 2: 70–75.
Kazem, S. & Hadinejad, F. 2015. PROMETHEE technique to select the best radial basis functions for solving the 2-dimensional heat equations based on Hermite interpolation. Engineering Analysis with Boundary Elements: 29-38.
Kahraman, S., Fener, M. & Gunaydin, O. 2004. Predicting the sawability of carbonate rocks using multiple curvilinear regression analysis. International Journal of Rock Mechanics & Mining Sciences. 41: 1123–1131.
Kahraman, S., Altun, H., Tezekici, B.S. & Fener, M. 2005. Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks. International Journal of Rock Mechanics & Mining Sciences. 43(1): 157–164.
Konstanty, J. 1991. The Materials Science of Stone Sawing. Industrial Diamond Review. 1: 27-31.
Mikaeil, R., Ataee, M. & Yousefi, R. 2010. Prediction of sawability of building stones by using the Fuzzy analytical hierarchy process. Publication of Geology of Engineering. 4(2): 1031-1058.
Mikaeil, R., Yousefi, R. & Ataei, M. 2011a. Sawability ranking of carbonate rock using fuzzy analytical hierarchy process and TOPSIS approaches. Scientia Iranica: 1106-1115.
Mikaeil, R., Yousefi, R., Ataei, M. & Abasian Farani, R. 2011b. Development of a New Classification System for Assessing of Carbonate Rock Sawability. Archives of Mining Sciences. 56(1): 57-68.
Mikaeil, R., Ataee, M. & Yousefi, R. 2012. Representation of statistical relations predict disk sawability of building stones. Scientific-Research Publication of Mine Engineering. 7(14): 31-40.
Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M. & Hosseini, S.M. 2013. Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making. International Journal of Rock Mechanics and Mining Sciences. 58: 118-126.
Mikaeil, R., Kamran, M. Abdollahi, Sadegheslam, G. & Ataei, M. 2015. Ranking sawability of dimension stone using PROMETHEE method. Journal of Mining & Environment. 6(2): 263-271.
Mohamedpur M. & Asgharizadeh, H. 2008. Rating in a research center of the Institute of multi-criteria decision-making methods ORESTE. Research Management. 1(1): 217-233.
Opricovic, S. 1998. Multi-criteria optimization of civil engineering systems. Faculty of Civil Engineering. 37(12): 1379–1383.
Opricovic, S. & Tzeng, G. H. 2002. Multi-criteria planning of post-earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering. 17(3): 211–220.
Opricovic, S. & Tzeng, G. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 156(2): 445–455.
Qazi Hussain, M. & Tabarsa, N. 2012. The evaluation and ranking of mineral water industry FAHP Using Fuzzy and VIKOR. National Conference on Entrepreneurship and Business Management Knowledge. Mazandaran University.
Rogers M.G., Bruen M. & Maystre L-Y. 1999. Electre and Decision Support: Methods and Applications in Engineering and Infrastructure Investment. Kluwer Academic Publishers. London.
Roy, B. 1968. Classement et choix en présence de points de vue multiples (la méthode ELECTRE). RAIRO-Operations Research-Recherche Opérationnelle. 2(1): 57–75.
Roy, B. 1991.The Outranking Approach and the Foundation of ELECTRE Methods. Theory and Decision, 31: 49-73.
Roubens, M. 1982. Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research. 10(1): 51-55.
Sun, L.a., Pan, J.b. & Lin, C. 2002. A new approach to improve the performance of diamond saw blades. Materials Letters. 57: 1010-1014.
Tonshoff, H.K. & Warnecke, G. 1982. Research on stone sawing. Advance in Ultra hard Materials Application Technology. P. Daniel (Ed). Harnbeam, England. 1: 36-49.
Tutmez, B., Kahraman, S. & Gunaydin, O. 2007. Multifactorial fuzzy approach to the sawability classification of building stones. Construction and Building Materials. 21: 1672-1679.
Van Delft A. & Nijkamp P. 1976. A Multi-Objective Decision Making Model for Regional Development. Environ- mental Quality Control and Industrial Lead Use. Papers Regional Associations. 36: 35-57.
Valentinas, P. 2011. The Comparative Analysis of MCDA Methods SAW and COPRAS. Inzinerine Ekonomika – Engineering Economics. 22: 134-146.
Zare Naghadehi, M., Mikaeil, R. & Ataei, M. 2009. The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran. Expert Systems with Applications: 8218-8226.
Zavadskas, E.K. & Turkis, Z. 2011. Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and Economic Development of Economy. 17(2): 397-427.