M
Mohammad Reza Hairi Yazdi
Researcher at University of Tehran
Publications - 172
Citations - 4297
Mohammad Reza Hairi Yazdi is an academic researcher from University of Tehran. The author has contributed to research in topics: Computer science & Fault tree analysis. The author has an hindex of 29, co-authored 143 publications receiving 2479 citations. Previous affiliations of Mohammad Reza Hairi Yazdi include Pasteur Institute of Iran & Tehran University of Medical Sciences.
Papers
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A fuzzy Bayesian network approach for risk analysis in process industries
TL;DR: The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty.
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Improved DEMATEL methodology for effective safety management decision-making
TL;DR: Application of the integrated DEMATEL methodology with Best-Worst method (BWM) and Bayesian network (BN) is illustrated by adopting a case study of safety management in the high-tech industry.
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An Extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) Application for Aircraft Landing System
TL;DR: In this paper, a group decision-making under the fuzzy environment was designed to contribute both theoretically and practically contributes to aircraft landing system as one of the important potential failure mode in aerospace industry.
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A hybrid model for human factor analysis in process accidents: FBN-HFACS
TL;DR: A hybrid dynamic human factor model considering Human Factor Analysis and Classification System, intuitionistic fuzzy set theory, and Bayesian network is presented, testing its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.
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Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach
TL;DR: The intuitionistic fuzzy hybrid TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is proposed to deal with limitations of a crisp risk matrix and uncertainties of group decision makers using experts’ opinions in linguistic terms to obtain an effective and comprehensive risk assessment technique.