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

Sanaz Nikfalazar, Hadi Akbarzade Khorshidi () and Ali Zeinal Hamadani
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Sanaz Nikfalazar: Faculty of Information Technology, Clayton campus, Monash University
Hadi Akbarzade Khorshidi: School of Applied Sciences and Engineering, Faculty of Science, Monash University
Ali Zeinal Hamadani: Isfahan University of Technology

International Journal of System Assurance Engineering and Management, 2016, vol. 7, issue 3, No 2, 250-256

Abstract: Abstract 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.

Keywords: Generalized trapezoidal fuzzy numbers; Similarity-based multi-criteria; Fuzzy risk analysis; Linguistic terms; Data mining (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-016-0414-6

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