4.7 Article

Restoration resource allocation model for enhancing resilience of interdependent infrastructure systems

Journal

SAFETY SCIENCE
Volume 102, Issue -, Pages 169-177

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ssci.2017.10.014

Keywords

Interdependent infrastructure system; System resilience; Resource allocation; Dynamic input-output model; Disruptive event

Funding

  1. Natural Science Foundation of China [71301095, 71704111]
  2. Natural Sciences and Engineering Research Council (NSERC) of Canada
  3. Chaucer PLC
  4. Institute of Catastrophic Loss Reduction (ICLR)

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Enhancing the resilience of infrastructure systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent infrastructure systems. According to Inoperability Input Output Model, a resilience metric for infrastructure systems is developed, in which the performance loss of infrastructure systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of infrastructure system; and (3) the significance of factors such as initial inoperability of infrastructure systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

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