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Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios

Author

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  • Mancuso, A.
  • Compare, M.
  • Salo, A.
  • Zio, E.

Abstract

This paper presents a methodology to support the selection of optimal portfolios of preventive safety measures for time-dependent accident scenarios. This methodology captures the dynamics of accident scenarios through Dynamic Bayesian Networks which represent the temporal evolution of component failures that can lead to system failure. An optimization model is presented to determine all Pareto optimal portfolios for which the residual risk of the system at different time stages is minimized, subject to budget and technical constraints on the set of feasible portfolios. The resulting portfolios are then analyzed to support the optimal selection of preventive safety measures. We also develop a computationally efficient algorithm for solving the multi-objective optimization model. The method is illustrated by revisiting the accident scenario of a vapour cloud ignition which occurred at Universal Form Clamp in Bellwood (Illinois, U.S.) on 14 June 2006. Results are presented for different cost levels of implementing preventive safety measures, which provides additional management insights.

Suggested Citation

  • Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2019. "Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    • Handle: RePEc:eee:reensy:v:190:y:2019:i:c:20
    DOI: 10.1016/j.ress.2019.106500
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    References listed on IDEAS

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    4. Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.
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    1. de la Barra, Joaquín & Salo, Ahti & Pourakbari-Kasmaei, Mahdi, 2026. "Choosing portfolios of reinforcement actions for distribution grids based on partial information," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    2. Euan Barlow & Alec Morton & Saudamini Dabak & Sven Engels & Wanrudee Isaranuwatchai & Yot Teerawattananon & Kalipso Chalkidou, 2022. "What is the value of explicit priority setting for health interventions? A simulation study," Health Care Management Science, Springer, vol. 25(3), pages 460-483, September.
    3. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Liu, Guanyi & Liu, Shifeng & Li, Xuewei & Li, Xueyan & Gong, Daqing, 2025. "Multiscenario deduction analysis for railway emergencies using knowledge metatheory and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    5. Valente, Matteo & Reniers, Genserik & Cozzani, Valerio, 2026. "Techno-Economic Analysis of Protection Barriers Against Fire-Induced Domino Effects in the Chemical and Process Industry," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    6. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2020. "Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Salo, Ahti & Andelmin, Juho & Oliveira, Fabricio, 2022. "Decision programming for mixed-integer multi-stage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(2), pages 550-565.

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