Multi-criteria decision making for the choice problem in mining and mineral processing: applications and trends
File(s)
Published version
Author(s)
Sitorous, Fernando
Cilliers, Jan J
Brito Parada, Pablo Rafael
Type
Journal Article
Abstract
Despite the fact that the potential of multi-criteria decision making (MCDM) to overcome a variety of problems in mining and mineral processing has been widely recognised, no literature review in these fields has been conducted. This manuscript addresses this issue by providing a comprehensive overview of the applications and trends of MCDM methods for the choice problem (i.e., determining the best option from a set) in mining and mineral processing. 90 articles published between 1999 and 2017 were selected following a searching methodology and eligibility criteria detailed in this manuscript. In addition, for the purpose of the survey, different types of selection problems were identified. The results show that there are two phases of growth in the application of MCDM techniques to the choice problem in mining and mineral processing. The first phase, from 1999 to 2007, shows a very low number of publications with only a moderate increase by the end, whereas the second phase, from 2007 to 2017, shows a significant growth in the number of published articles. The review also shows that the most addressed problem has been the selection of mining methods, while the Analytical Hierarchy Process (AHP) has been the most used MCDM method. The rise in the application of hybrid MCDM methods is also discussed. This review paper provides insight into the current state of applications of MCDM in mining and mineral processing and discusses pathways for future research directions in the development of MCDM methods that would benefit these fields.
Date Issued
2019-05-01
Date Acceptance
2018-12-01
Citation
Expert Systems with Applications, 2019, 121, pp.393-417
ISSN
0957-4174
Publisher
Elsevier
Start Page
393
End Page
417
Journal / Book Title
Expert Systems with Applications
Volume
121
Copyright Statement
© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Sponsor
EC‘s Framework Programme for Research and Innovation Horizon 2020
Grant Number
637077
Subjects
01 Mathematical Sciences
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2018-12-06