An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers

  • Authors:

  • S. R. Hejazi;A. Doostparast;S. M. Hosseini

  • Affiliations:

  • Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran;Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran;Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

This paper presents a novel method of fuzzy risk analysis based on a new similarity measure of generalized fuzzy numbers. This similarity measure considers many features of generalized fuzzy numbers such as the area, perimeter, height and geometric distance of these kinds of fuzzy numbers. Using some sets of generalized fuzzy numbers, we show the power of this similarity measurement method to overcome the drawbacks that other methods are suffering. Applying the proposed method, we present an improved fuzzy risk analysis method which develops the capability of fuzzy risk analysis methods to deal with sophisticated problems. In the proposed method we use new factors such as probability of failure detection and economic disbenefits of failure occurrence which have not been used in fuzzy risk analysis methods before.