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A maximal multiple coverage and network restoration problem for disaster recovery

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  • Iloglu, Suzan
  • Albert, Laura A.

Abstract

The recovery of physically damaged infrastructure after disasters is critical to efficiently deliver disaster relief supplies and emergency services. The physical damage to road infrastructure from disasters can result in decreased road link capacities and an inability to meet the community’s emergency demand. This paper provides an infrastructure restoration plan for delivering critical services after disasters. We present a maximal multiple coverage and network recovery problem for the recovery and restoration of infrastructure systems after disasters. In the model, recovery crews make damaged arcs available by repairing components over a time horizon in a disrupted network. The model relocates emergency responders using the available arcs in the network to maximize multiple coverage of emergency service demand over the time horizon. We present two heuristics for the model. The first uses the Lagrangian and the linear programming relaxation solutions of the problem, and the second uses an integer rounding procedure applied to the linear programming relaxation solution. We test the model using a real-world example representing the road infrastructure and emergency services of the Bronx Borough in New York, NY during Hurricane Sandy. The results demonstrate that the integer rounding heuristic is effective in identifying near-optimal solutions. Our computational study suggests that our model can aid emergency managers in achieving their goals by scheduling effective restoration activities for real-time disaster recovery and long-term recovery planning.

Suggested Citation

  • Iloglu, Suzan & Albert, Laura A., 2020. "A maximal multiple coverage and network restoration problem for disaster recovery," Operations Research Perspectives, Elsevier, vol. 7(C).
    • Handle: RePEc:eee:oprepe:v:7:y:2020:i:c:s2214716019301198
    DOI: 10.1016/j.orp.2019.100132
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    3. Alkhaleel, Basem A. & Liao, Haitao & Sullivan, Kelly M., 2022. "Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty," European Journal of Operational Research, Elsevier, vol. 296(1), pages 174-202.
    4. Rodelia Sansano & Makoto Chikaraishi, 2022. "Exploring Natural and Social Factors Affecting Road Disruption Patterns and the Duration of Recovery: A Case from Hiroshima, Japan," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    5. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Souza Almeida, Luana & Goerlandt, Floris & Pelot, Ronald, 2022. "Trends and gaps in the literature of road network repair and restoration in the context of disaster response operations," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    7. Garay-Sianca, Aniela & Nurre Pinkley, Sarah G., 2021. "Interdependent integrated network design and scheduling problems with movement of machines," European Journal of Operational Research, Elsevier, vol. 289(1), pages 297-327.

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