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A Dynamic Network Flow Model for Interdependent Infrastructure and Supply Chain Networks with Uncertain Asset Operability

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Computational Logistics (ICCL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10572))

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Abstract

In globally integrated supply chain networks, initially local disruptions can quickly escalate to major problems due to complex interdependencies and cascading failure. This paper is particularly concerned with the role of infrastructure failure causing or exacerbating such cascading effects in supply chain networks. To improve the understanding of infrastructure and supply chain interdependency, we propose a novel modelling approach that captures the dynamics of both asset operability and network flows. The method uses a Markov process to generate operability scenarios and a multistage stochastic linear program to assign dynamic flows and optimise network capacities. The model takes into account different mechanisms of cascading failure, namely failure propagation, delay of recovery and unavailability of production inputs. A numeric example demonstrates how the method can be used to assess and optimises the resilience of a global supply chain against multiple hazards and infrastructure failure.

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Authors and Affiliations

  1. Centre for Transport Studies (CTS), Imperial College London, London, UK

    Nils Goldbeck, Panagiotis Angeloudis & Washington Y. Ochieng

Authors
  1. Nils Goldbeck
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  2. Panagiotis Angeloudis
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  3. Washington Y. Ochieng
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Corresponding author

Correspondence to Nils Goldbeck .

Editor information

Editors and Affiliations

  1. University of Southampton , Southampton, United Kingdom

    Tolga BektaÅŸ

  2. University of Southampton , Southampton, United Kingdom

    Stefano Coniglio

  3. University of Southampton , Southampton, United Kingdom

    Antonio Martinez-Sykora

  4. University of Hamburg , Hamburg, Germany

    Stefan Voß

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Goldbeck, N., Angeloudis, P., Ochieng, W.Y. (2017). A Dynamic Network Flow Model for Interdependent Infrastructure and Supply Chain Networks with Uncertain Asset Operability . In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds) Computational Logistics. ICCL 2017. Lecture Notes in Computer Science(), vol 10572. Springer, Cham. https://doi.org/10.1007/978-3-319-68496-3_34

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  • DOI: https://doi.org/10.1007/978-3-319-68496-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68495-6

  • Online ISBN: 978-3-319-68496-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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