FMMSIC: a hybrid fuzzy based decision support system for MMS (in order to estimate interrelationships between criteria)

Namin F. S., Shahriar K., Başçetin A., Ghodsypour S. H.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, vol.63, no.2, pp.218-231, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 63 Issue: 2
  • Publication Date: 2012
  • Doi Number: 10.1057/jors.2011.24
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.218-231
  • Keywords: hybrid decision support system, fuzzy entropy, fuzzy ANP, modified TOPSIS, mining method selection, FMMSIC, TOPSIS METHOD, MATERIAL SELECTION, MODEL, SETS
  • Istanbul Technical University Affiliated: Yes


One of the main tasks in exploitation of ore-body is to select a suitable mining method. In mining method selection (MMS) problems, a decision procedure has to choose the best exploitation method that satisfies the evaluation criteria. It is generally hard to find a mining method that meets all the criteria simultaneously, therefore a good compromise solution is preferred as the final selection. Furthermore, the MMS problem is an inherently uncertain activity. To deal with the uncertainty, this paper presents an hybrid decision support system based on the fuzzy multi attribute decision making, named the fuzzy mining method selection with interrelation criteria (FMMSIC). FMMSIC models the relative weights of criteria by combining the fuzzy analytic network process and fuzzy entropy, and discusses using these hybrid techniques to determine the overall weights. Subsequently, the technique for order preference by similarity to an ideal solution method was modified by various normalization norms according to the MMS problem condition. Finally, to illustrate how the FMMSIC is used for the MMS problems, an empirical study of a real case is conducted. It shows by means of an application that the FMMSIC is well suited as a decision support system for the MMS. Journal of the Operational Research Society (2012) 63, 218-231. doi: 10.1057/jors.2011.24 Published online 4 May 2011