Fuzzy risk analysis by similarity-based multi-criteria approach to classify alternatives

Sanaz Nikfalazar, Hadi Akbarzadeh Khorshidi, Ali Zeinal Hamadani

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


In this paper, a new method for fuzzy risk analysis based on similarity of fuzzy numbers is presented. First of all, the similarity-based multi-criteria approach is used to find the similarity between generalized trapezoidal fuzzy numbers and the desired fuzzy number to rank them. The geometry distance, spread difference, perimeter ratio, area ratio, and height ratio have been considered as criteria. Secondly, the proposed ranking approach is used for fuzzy risk analysis. Severity of loss, probability of failure, and failure ignorance possibility are the parameters to assess the risk of various alternatives. In the proposed fuzzy risk analysis, the alternatives can be classified into different levels in terms of their risk. Therefore, fuzzy risk analysis leads to classification and prioritization of the alternatives. Finally, a numerical example is presented to illustrate the applicability of the proposed method.

Original languageEnglish
Pages (from-to)250-256
Number of pages7
JournalInternational Journal of Systems Assurance Engineering and Management
Issue number3
Publication statusPublished - 1 Sep 2016


  • Data mining
  • Fuzzy risk analysis
  • Generalized trapezoidal fuzzy numbers
  • Linguistic terms
  • Similarity-based multi-criteria

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