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A risk-averse stochastic optimization model for community resilience planning

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  • Faiz, Tasnim Ibn
  • Harrison, Kenneth W.

Abstract

Community resilience planning is challenging as it involves several large-scale systems with interdependency, populations with diverse socio-economic characteristics, and numerous stakeholders. This study introduces a new optimization model to decrease a community's burden in developing viable alternative sets of decisions while considering costs and risks associated with uncertain hazard events. The model captures the essential features of a community, and its scope extends beyond infrastructure and buildings to include social goals. Structural engineering and social science approaches are adapted and incorporated into the model formulation to facilitate the identification of engineering decisions meeting the social goals of minimizing population dislocation and time for recovery. A risk-averse approach frames the optimization problem as a two-stage mean-risk stochastic programming model, which enables effective planning for low-probability, high-consequence hazard events. A case study simulating flood hazards in Lumberton, North Carolina, is developed, and the model is run with the generated data set to showcase the model's capability in developing risk-informed mitigation and recovery plans to achieve resilience goals. The insights drawn from the numerical experiments show the effect of changing risk preference on community resilience metrics.

Suggested Citation

  • Faiz, Tasnim Ibn & Harrison, Kenneth W., 2024. "A risk-averse stochastic optimization model for community resilience planning," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:soceps:v:92:y:2024:i:c:s003801212400034x
    DOI: 10.1016/j.seps.2024.101835
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    References listed on IDEAS

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    1. Camilo Gomez & Andrés D. González & Hiba Baroud & Claudia D. Bedoya‐Motta, 2019. "Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1913-1929, September.
    2. Andrew M. Smith & Andrés D. González & Leonardo Dueñas‐Osorio & Raissa M. D'Souza, 2020. "Interdependent Network Recovery Games," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 134-152, January.
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    4. Yasser Almoghathawi & Andrés D. González & Kash Barker, 2021. "Exploring Recovery Strategies for Optimal Interdependent Infrastructure Network Resilience," Networks and Spatial Economics, Springer, vol. 21(1), pages 229-260, March.
    5. Guidotti, Roberto & Gardoni, Paolo & Rosenheim, Nathanael, 2019. "Integration of physical infrastructure and social systems in communities’ reliability and resilience analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 476-492.
    6. Kong, Jingjing & Zhang, Chao & Simonovic, Slobodan P., 2021. "Optimizing the resilience of interdependent infrastructures to regional natural hazards with combined improvement measures," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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