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R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.
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- Author(s): Seiti, Hamidreza1 (AUTHOR); Makui, Ahmad1 (AUTHOR) ; Hafezalkotob, Ashkan2 (AUTHOR); Khalaj, Mehran3 (AUTHOR); Hameed, Ibrahim A.4 (AUTHOR)
- Source:
Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B. Mar2022, Vol. 159, p585-604. 20p.- Subject Terms:
- Source:
- Additional Information
- Abstract: Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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