Risk-informed optimization of mitigation strategies in safety-critical systems

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorCompare, Michele , Dr., Politecnico di Milano, Italy
dc.contributor.advisorZebrowski, Piotr, Dr., International Institute for Applied Systems Analysis, Austria
dc.contributor.authorMancuso, Alessandro
dc.contributor.departmentMatematiikan ja systeemianalyysin laitosfi
dc.contributor.departmentDepartment of Mathematics and Systems Analysisen
dc.contributor.labSystems Analysis Laboratoryen
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorSalo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Zio, Enrico, Prof., Politecnico di Milano, Italy
dc.date.accessioned2020-08-27T09:00:05Z
dc.date.available2020-08-27T09:00:05Z
dc.date.defence2020-09-18
dc.date.issued2020
dc.descriptionThe public defense will be also organized via remote technology. Link: https://aalto.zoom.us/j/69611217934 Zoom quick guide: https://www.aalto.fi/en/services/zoom-quick-guide
dc.description
dc.description.abstractIndustrial organizations need to invest in the design and operations of their production systems to improve reliability, availability, maintainability and safety. Typically, these organizations have limited resources, therefore they can select only a subset of mitigation actions to protect the system from the risks associated with accident and threat scenarios. For this reason, optimization models for resource allocation are necessary to minimize the risks of such scenarios. In current practices, resources are often allocated based on the failure risk of the individual components, which can lead to sub-optimal solutions. By contrast, this Dissertation proposes systemic analyses of accident and threat scenarios in order to determine the optimal mitigation strategy for the overall system. The optimal strategy is a combination (portfolio) of mitigation actions for system design and operations that minimize the systemic risks, while satisfying relevant budgetary and technical constraints. For this purpose, the probabilistic analysis of the systemic risks is performed through Bayesian models to capture the uncertainties of the accident and threat scenarios. Then, the selection of the optimal resource allocation builds on Portfolio Decision Analysis to determine the optimal portfolios consisting of a set of discrete alternatives. In addition, the methodologies allow a range of sensitivity analyses on budget allocation and risk management of the accident and threat scenarios. The methodologies are illustrated by revisiting real-life case studies and reported examples in the context of system design and operations, to demonstrate that systemic analyses enhance the current practices on component-based resource allocation. The methodologies are also generic in that they can be employed in other application areas with reasonable adaptations.en
dc.format.extent48 + app. 104
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn978-952-60-3985-5 (electronic)
dc.identifier.isbn978-952-60-3984-8 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/46250
dc.identifier.urnURN:ISBN:978-952-60-3985-5
dc.language.isoenen
dc.opnWalls, Lesley, Prof., University of Strathclyde, UK
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Alessandro Mancuso, Michele Compare, Ahti Salo and Enrico Zio. Portfolio optimization of safety measures for reducing risks in nuclear systems. Reliability Engineering and System Safety, 167:20-29, November 2017. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002282310. DOI: 10.1016/j.ress.2017.05.005
dc.relation.haspart[Publication 2]: Alessandro Mancuso, Piotr Zebrowski and Aitor Couce Vieira. Risk-based selection of mitigation strategies for cybersecurity of electric power systems. Manuscript, 25 pages, May 2019
dc.relation.haspart[Publication 3]: Alessandro Mancuso, Michele Compare, Ahti Salo and Enrico Zio. Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios. Reliability Engineering and System Safety, 190(106500):1- 9, October 2019. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002282313. DOI: 10.1016/j.ress.2019.106500
dc.relation.haspart[Publication 4]: Alessandro Mancuso, Michele Compare, Ahti Salo and Enrico Zio. Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system. Data in Brief, 25(104243):1-5, August 2019. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002031948. DOI: 10.1016/j.dib.2019.104243
dc.relation.haspart[Publication 5]: Alessandro Mancuso, Michele Compare, Ahti Salo, Enrico Zio and Tuija Laakso. Risk-based optimization of pipe inspections in large underground networks with imprecise information. Reliability Engineering and System Safety, 152:228-238, August 2016. DOI: 10.1016/j.ress.2016.03.011
dc.relation.haspart[Publication 6]: Alessandro Mancuso, Michele Compare, Ahti Salo and Enrico Zio. Optimal Prognostics and Health Management-driven inspection and maintenancestrategies for industrial systems. Manuscript, 25 pages, December 2019
dc.relation.ispartofseriesAalto University publication series DOCTORAL DISSERTATIONSen
dc.relation.ispartofseries116/2020
dc.revReniers, Genserik. Prof., Delft University, Netherlands
dc.revMousseau, Vincent, Prof., Centrale Supelec, France
dc.subject.keywordrisk managementen
dc.subject.keywordsafety-critical systemsen
dc.subject.keywordBayesian networksen
dc.subject.keywordportfolio decision analysisen
dc.subject.keywordconstrained optimizationen
dc.subject.otherMathematicsen
dc.titleRisk-informed optimization of mitigation strategies in safety-critical systemsen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2020-10-19_1132
local.aalto.archiveyes
local.aalto.formfolder2020_08_26_klo_14_15
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