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The selection of a mining machine is a multiple-attribute problem that involves the consideration of numerous parameters of various origins. A common task in the mining industry is to select the best machine among several alternatives, which are frequently described both with numerical variables as well as linguistic variables. Numerical variables are mostly related to the technical characteristics of the machines, which are available in detail in most cases. On the other hand, some equally important parameters such as price, reliability, support for service and spare parts, operating cost, etc., are not available at the required level for various reasons; hence, these can be considered uncertain information. For this reason, such information is described with linguistic variables. This paper presents research related to overcoming this problem by using grey theory for selecting a proper mining machine. Grey theory is a well-known method used for multiple-attribute selection problems that involves a system in which parts of the necessary information are known and parts are unknown.
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Tom
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59--64
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Bibliogr. 15 poz., tab.
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- Faculty of Mining and Geology University of Belgrade Ðušina 7, 11000 Belgrade, Serbia
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- Faculty of Engineering Technology University of Twente PO BOX 217, 7500 AE Enschede, the Netherlands
autor
- Faculty of Mining and Geology University of Belgrade Ðušina 7, 11000 Belgrade, Serbia
Bibliografia
- [1] Dagdeviren M.: Decision making in equipment selection: an integrated approach with AHP and PROMETHEE, “Journal of Intelligent Manufacturing” 2008, 19: 397–406.
- [2] Lin Z.C., Yang C.B.: Evaluation of machine selection by the AHP method, “Journal of Materials Processing Technology” 1996, 57: 253–258.
- [3] Paramasivam V. et al.: Decision making in equipment selection: an integrated approach with digraph and matrix approach, AHP and ANP, “International Journal of Advanced Manufacturing Technology” 2011, 54: 1233–1244.
- [4] Bascetin A.: A decision support system for optimal equipment selection in open pit mining: analytical hierarchy process, “Istanbul Üniversitesi, Mühendislik Fakültesi Yer Bilimleri Dergisi” 2003, 16, 2: 1–11.
- [5] Mohamadabadi H.S. et al.: Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles, “Energy” 2009, 24, 1: 112–125.
- [6] Cebesoy T.: Hydraulic Excavator Selection Using Improved Quality Comparison Method, “Journal of Engineering Sciences (Mühendislik Bilimleri Dergisi)” 1999, 5, 1: 1001–1008.
- [7] Hadi-Vencheha A., Mohamadghasemi A.: A new hybrid fuzzy multi-criteria decision-making model for solving the material handling equipment selection problem, “International Journal of Computer Integrated Manufacturing” 2015, 28, 5: 534–550.
- [8] Basu A.S., Lineberry G.T.: Selection of Mobile Equipment for Underground Coal Mining: An Expert System Approach, “Mineral Resources Engineering” 1995, 4, 1: 71–88.
- [9] Delgado M., Verdegay J.L., Vila M.A.: Linguistic decisionmaking models, “International Journal of Intelligent Systems” 1992, 7: 479–492.
- [10] Hwang C.L., Yoon K.P.: Multiple Attributes Decision Making: Methods and Applications, Springer, Berlin – Heidelberg 1981.
- [11] Kaufmann A., Gupta M.M.: Introduction to Fuzzy Arithmetic, Theory and Applications, Van Nostrand Reinhold, New York 1991.
- [12] Liu S., Lin Y.: Grey systems Theory and Applications, Springer, Berlin – Heidelberg 2010.
- [13] Bhattacharyya R.: A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection, “Fuzzy Information and Engineering” 2015, 7: 211–225.
- [14] Li G-D. et al.: A grey-based decision-making approach to the supplier selection problem, “Mathematical and Computer Modelling” 2007, 46: 573–581.
- [15] Milisavljević V., Medenica D., Čokorilo V., Ristović I.: New Approach to Equipment Quality Evaluation Method with Distinct Function, “Thermal Science” 2015, 20, 2: 743–752.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-40a51524-9fec-4151-a454-01f1556a2bd5