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Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

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  • Houssein Abdo
  • Jean-Marie Flaus

Abstract

Quantifying uncertainty during risk analysis has become an important part of effective decision-making and health risk assessment. However, most risk assessment studies struggle with uncertainty analysis and yet uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is vital in order to perform a sound risk analysis. In this paper, an approach to uncertainty analysis based on the fuzzy set theory and the Monte Carlo simulation is proposed. The question then arises as to how these two modes of representation of uncertainty can be combined for the purpose of estimating risk. The proposed method is applied to a propylene oxide polymerisation reactor. It takes into account both stochastic and epistemic uncertainties in the risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment in industrial plants with a dynamic system (change over time). It discusses the methodology and the process involved when using random and fuzzy logic systems for risk management.

Suggested Citation

  • Houssein Abdo & Jean-Marie Flaus, 2016. "Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5862-5885, October.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:19:p:5862-5885
    DOI: 10.1080/00207543.2016.1184348
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    Cited by:

    1. Amit Kundu & Dev Narayan Sarkar & Arabinda Bhattacharya, 2021. "The effect of uncertainty on the formulation of strategies: a study of selected Indian organizations," SN Business & Economics, Springer, vol. 1(1), pages 1-21, January.
    2. Jorge Antunes & Goodness C. Aye & Rangan Gupta & Peter Wanke & Yong Tan, 2020. "Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach," Working Papers 2020111, University of Pretoria, Department of Economics.
    3. Amin Mahmoudi & Saad Ahmed Javed, 2023. "Uncertainty Analysis in Group Decisions through Interval Ordinal Priority Approach," Group Decision and Negotiation, Springer, vol. 32(4), pages 807-833, August.

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