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

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School of Science | Doctoral thesis (article-based) | Defence date: 2020-09-18
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Author
Date
2020
Department
Matematiikan ja systeemianalyysin laitos
Department of Mathematics and Systems Analysis
Major/Subject
Mcode
Degree programme
Language
en
Pages
48 + app. 104
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 116/2020
Abstract
Industrial 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.
Description
The 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
Supervising professor
Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Zio, Enrico, Prof., Politecnico di Milano, Italy
Thesis advisor
Compare, Michele , Dr., Politecnico di Milano, Italy
Zebrowski, Piotr, Dr., International Institute for Applied Systems Analysis, Austria
Keywords
risk management, safety-critical systems, Bayesian networks, portfolio decision analysis, constrained optimization
Other note
Parts
  • [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 View at publisher
  • [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
  • [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 View at publisher
  • [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 View at publisher
  • [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 View at publisher
  • [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
Citation
Permanent link to this item
https://urn.fi/URN:ISBN:978-952-60-3985-5
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[diss] Perustieteiden korkeakoulu / SCI