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19.06.2018 | Focus

A new method of level-2 uncertainty analysis in risk assessment based on uncertainty theory

verfasst von: Qingyuan Zhang, Rui Kang, Meilin Wen

Erschienen in: Soft Computing | Ausgabe 17/2018

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Abstract

The objective of this study is to present a novel method of level-2 uncertainty analysis in risk assessment by means of uncertainty theory. In the proposed method, aleatory uncertainty is characterized by probability distributions, whose parameters are affected by epistemic uncertainty. These parameters are described as uncertain variables. For monotone risk models, such as fault trees or event trees, the uncertainty is propagated analytically based on the operational rules of uncertain variables. For non-monotone risk models, we propose a simulation-based method for uncertainty propagation. Three indexes, i.e., average risk, value-at-risk and bounded value-at-risk, are defined for risk-informed decision making in the level-2 uncertainty setting. Two numerical studies and an application on a real example from literature are worked out to illustrate the developed method. A comparison is made to some commonly used uncertainty analysis methods, e.g., the ones based on probability theory and evidence theory.

Vorheriger Artikel Uncertain multivariable regression model

Nächster Artikel Belief degree of optimal models for uncertain single-period supply chain problem

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Titel: A new method of level-2 uncertainty analysis in risk assessment based on uncertainty theory
verfasst von: Qingyuan Zhang
Rui Kang
Meilin Wen
Publikationsdatum: 19.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 17/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3337-0

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