Research Area: Metaheuristic Computing
Critical infrastructure systems are important for continuous functioning of the modern society. This paper proposes a model for developing infrastructure resilience improvement strategy to natural hazards on a regional scale, with combined pre- and post-disaster resilience improvement measures (RIMs). The main objective of the model is to maximize the expected value of the resilience of interdependent infrastructure systems. The interdependencies between infrastructure systems, the requirements on the proactive and reactive capacities of the interdependent systems, and the limit of specialized resources are incorporated in the model. A numerical solution method applying heuristics and Monte Carlo simulations is presented. The proposed model is validated using a case study on the Greater Toronto Area energy infrastructure systems. The results show that: (i) comparing with individual RIM, the combined RIMs can increase the resilience value of interdependent infrastructure systems while keeping the system properties within acceptable levels; (ii) the optimal combination of RIMs changes with the requirements on the proactive and reactive capacities; and (iii) the intensity of natural hazards and the response time of restoration activities have significant impacts on the optimal combination of RIMs. The proposed model can assist the decision makers to select effective combined RIMs under different regional natural hazard scenarios.
Keywords:
resilience improvement measures (RIMs)
heuristics
Monte Carlo simulations
natural hazard
Author(s) Name: Jingjing Kong, Chao Zhang, Slobodan P. Simonovic
Journal name: Reliability Engineering & System Safety
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.ress.2021.107538
Volume Information: Volume 210, June 2021, 107538
