Equipment Selection Using Fuzzy Multi Criteria Decision Making Model: Key Study of Gole Gohar Iron Min
DOI:
https://doi.org/10.5755/j01.ee.23.2.1544Keywords:
MCDM, Fuzzy Sets, AHP, ANP, TOPSIS, Integrated Model, Equipment, SelectionAbstract
Loading and hauling contribute significantly towards expenses in surface mines. Thus selecting the most suitable system which minimizes the cost per ton and meets production needs is one of the main concerns of mine design and planning. It is also at times difficult to select the optimum equipment, as there are many possible options and influencing factors in selecting a system. Furthermore, some of these factors can be either quantitative or qualitative. As a result, the use of multi attribute decision making methods can be useful. In this article, the selection of the equipment fleet of Gole Gohar mine was done though four stages. First, feasible technical and operational options were determined. Next, the weights of influential criteria were determined using a hybrid method of fuzzy analytical hierarchical process and analytical network process. Then, the alternative preference rating matrix was calculated using fuzzy TOPSIS method and finally, the hierarchy of alternatives was decided by combining the available weight and ranking matrix. This model considers all affecting parameters simultaneously and facilitates making a reasonable decision about the most appropriate material handling equipment. For the purpose of evaluation in this method, the cost of each equipment fleet was assessed and compared using the traditional method. Results show that the use of the fleet of cable shovel and truck is the most economical loading and hauling system. The results not only indicate that proposed model offers chances to choose the best alternative among possible loading and hauling systems, but also help equipment managers to make an accurate and reasonable decision regarding all effective parameters.Additional Files
Published
2012-04-18
Issue
Section
ECONOMICS OF ENGINEERING DECISIONS