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- Title
Comparative Analysis of AHP and TOPSIS Multi-Criteria Decision-Making Methods for Mining Method Selection.
- Authors
Ccatamayo-Barrios, Johnny-Henrry; Huamán-Romaní, Yersi-Luis; Cruz Yupanqui, Gladys Marcionila; Seminario-Morales, María-Verónica; Flores-Castillo, Marcos-Marcelo; Gutiérrez-Gómez, Edgar; Carrillo-De la cruz, Linda-Katherine; Maricielo Nole-Jaramillo, Juliana; de la Cruz-Girón, Kevin-Arnold
- Abstract
In the realm of mineral resource extraction, the adoption of decision-making methodologies for the selection of appropriate mining techniques has garnered considerable attention in recent decades. This study aims to provide a comparative assessment of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as multi-criteria decision-making methods in the context of mining method selection. Case in point, the Gol Gohar Iron Mine No. 3, situated in Kerman province, was chosen for this investigation. In light of numerical scoring procedures, three primary extraction methods — Open Pit, Cut & Fill, and Sublevel Caving — were selected for initial consideration. Subsequently, these primary options were evaluated using decision-making models, taking into account management indicators. The goal was to determine the ranking and efficacy of these models in the selection of the extraction method. Our findings suggest that the AHP method, premised on paired comparisons, exhibits superior efficacy in mining method selection compared to the TOPSIS method. Furthermore, due to the interaction between indicators, the AHP method is recommended as the more suitable model relative to the TOPSIS method. Upon implementation of the model, the Open Pit mining method emerged as the most optimal extraction method for the anomaly No. 3 of Gol Gohar Iron Mine.
- Subjects
ANALYTIC hierarchy process; TOPSIS method; MINES &; mineral resources; DECISION making; STRIP mining; IRON mining; COMPARATIVE studies
- Publication
Mathematical Modelling of Engineering Problems, 2023, Vol 10, Issue 5, p1665
- ISSN
2369-0739
- Publication type
Article
- DOI
10.18280/mmep.100516