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International Conference on Computational Logistics

ICCL 2019: Computational Logistics pp 428–442Cite as

Multi-Stage Recovery Resilience: A Case Study of the Dique Canal

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11756))

Abstract

Disasters may cause severe damage to a community with long lasting consequences that will impede a short recovery. In high magnitude events the total recovery time is measured in months or years. These long recoveries are performed in multiple stages and different recovery rates. Traditional resilience metrics have been designed to study strictly increasing recovery functions, which is not a valid assumption for the multi-stage recovery case. This paper defines a model to measure resilience for a multi-stage recovery scenario, where the recovery process is performed in two or more stages with possibly different recovery rates. A linear approximation metric is proposed to improve resilience level estimation accuracy. The new metric is tested on a case study of the Dique Canal breach in Colombia occurred in 2010. The adapted resilience metric is combined with an optimization model to maximize the average performance on multi-stage scenarios, enabling decision makers to decide the best strategy per stage for a predefined budget. A comparative analysis confirms that the proposed model offers a better resilience estimation than previous linear average performance metrics.

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Acknowledgements

The authors thank the referees for their helpful comments that significantly contributed to improving the quality of the paper. The work by Daniel Romero-Rodriguez has been partially supported by the Colombian Government through the COLCIENCIAS-scholarship Convocatoria 2015-CC1129579108 and cofinanced by the Universidad del Norte through the XVII Convocatoria Interna de Investigación UNINORTE. The work by Julio-Mario Daza-Escorcia has been partially supported by the Colombian Government through the COLCIENCIAS-scholarships CONV-617-2013-CAP3-CC72357251.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Universidad del Norte, Barranquilla, Colombia

    Daniel Romero-Rodriguez

  2. Department of Industrial Management and Systems Engineering, University of South Florida, Tampa, Florida, USA

    Alex Savachkin, Weimar Ardila-Rueda & Alvaro Sierra-Altamiranda

  3. Program of Industrial Engineering, Konrad Lorenz University Foundation, Bogotá D.C, Colombia

    Julio-Mario Daza-Escorcia

Authors
  1. Daniel Romero-Rodriguez
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  2. Alex Savachkin
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  3. Weimar Ardila-Rueda
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  4. Alvaro Sierra-Altamiranda
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  5. Julio-Mario Daza-Escorcia
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Corresponding author

Correspondence to Daniel Romero-Rodriguez .

Editor information

Editors and Affiliations

  1. Universidad del Norte, Barranquilla, Colombia

    Carlos Paternina-Arboleda

  2. Universität Hamburg, Hamburg, Germany

    Stefan Voß

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Romero-Rodriguez, D., Savachkin, A., Ardila-Rueda, W., Sierra-Altamiranda, A., Daza-Escorcia, JM. (2019). Multi-Stage Recovery Resilience: A Case Study of the Dique Canal. In: Paternina-Arboleda, C., Voß, S. (eds) Computational Logistics. ICCL 2019. Lecture Notes in Computer Science(), vol 11756. Springer, Cham. https://doi.org/10.1007/978-3-030-31140-7_27

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  • DOI: https://doi.org/10.1007/978-3-030-31140-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31139-1

  • Online ISBN: 978-3-030-31140-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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