Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/85467
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dc.contributorDepartment of Computing-
dc.creatorZhou, Peng-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/7228-
dc.language.isoEnglish-
dc.titleManaging time elements of risk-
dc.typeThesis-
dcterms.abstractAlthough current risk management has well defined process life cycle, already considers the need to continuously monitor the risk indicators and periodically identify new risks and re-estimate identified risks, most practitioners and researchers seldom explicitly model and use many time elements of risk. The modeling and management of time elements is essential for risk management since each risk has an associated time period of mitigation and occurrence. However, there are very few studies explicitly model these time elements. Also, there is a lack of theories for performing risk management with due consideration of them. To address the limitation of current risk management practices, this thesis aims to enhance the performance of risk management. We explicitly model the time elements of risk by (1) identifying them during the whole life cycle of a risk, (2) establishing the relationships between them, (3) creating different risk mitigation cases and presenting possible transition between these cases based on the established relationships, and (4) developing the status change diagram of risk and analyzing the possible status change paths. We also identify and summarize the management of time elements in the risk management life cycle. Additionally, to facilitate the formal analysis, we build a stochastic simulation model, SMRMP, and validate and verify it based on the paradigm proposed by Sargent. We formally analyze how time elements affect risk mitigation at both risk-level and project-level. The results show that the traditionally used strategy for scheduling risk mitigation is not a good choice. From the results of formal analysis, we propose new practices for risk mitigation to enhance the effectiveness of risks management. At last, we extend our results by excluding two assumptions made in our study.-
dcterms.accessRightsopen access-
dcterms.educationLevelPh.D.-
dcterms.extentix, 171 p. : ill. ; 30 cm.-
dcterms.issued2013-
dcterms.LCSHRisk management.-
dcterms.LCSHRisk management -- Simulation methods.-
dcterms.LCSHHong Kong Polytechnic University -- Dissertations-
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