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

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-guideIndustrial 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