Selection of risk reduction portfolios under interval-valued probabilities. (July 2017)

• Record Type: Journal Article
• Authors: Toppila, Antti; Salo, Ahti
• Is Part Of: Reliability engineering & system safety. Volume 163(2017)
• Page Start: 69
• Page End: 78
• Publication Date: 2017-07
• Subjects: Epistemic uncertainty -- Interval-valued probabilities -- Reliability allocation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.02.005
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: A central problem in risk management is that of identifying the optimal combination (or portfolio) of improvements that enhance the reliability of the system most through reducing failure event probabilities, subject to the availability of resources. This optimal portfolio can be sensitive with regard to epistemic uncertainties about the failure events' probabilities. In this paper, we develop an optimization model to support the allocation of resources to improvements that mitigate risks in coherent systems in which interval-valued probabilities defined by lower and upper bounds are employed to capture epistemic uncertainties. Decision recommendations are based on portfolio dominance: a resource allocation portfolio is dominated if there exists another portfolio that improves system reliability (i) at least as much for all feasible failure probabilities and (ii) strictly more for some feasible probabilities. Based on non-dominated portfolios, recommendations about improvements to implement are derived by inspecting in how many non-dominated portfolios a given improvement is contained. We present an exact method for computing the non-dominated portfolios. We also present an approximate method that simplifies the reliability function using total order interactions so that larger problem instances can be solved with reasonable computational effort. Abstract : Highlights: Reliability allocation under epistemic uncertainty about probabilities. Comparison of alternatives using dominance. Computational methods for generating the non-dominated alternatives. Deriving decision recommendations that are robust with respect to epistemic uncertainty.