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Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics

Author

Listed:
  • Alexander Pavlov

    (Saint Petersburg Institute of Automation and Informatics (SPIIRAS)
    Mozhaisky Aerospace Academy)

  • Dmitry Ivanov

    (School of Economics and Law)

  • Dmitry Pavlov

    (Mozhaisky Aerospace Academy)

  • Alexey Slinko

    (Mozhaisky Aerospace Academy)

Abstract

One of the key issues in supply chain sustainability is the efficient usage of the available resources. At the same time, proactive supply chain design with disruption risk considerations frequently leads to a network redundancy which implies some resource reservations in anticipation of possible disruptions. Even if resilient supply chain design has received much attention in literature, there is a research gap in designing both resilient and sustainable supply chains. This study contributes to closing the given gap by proposing a novel methodological approach to modelling network redundancy optimization. This allows for simultaneous computation of both optimal network redundancy and proactive contingency plans, considering both supply dynamics and structural disruption risks. The novelties of this study are the integration of sustainable resource utilization and SC resilience based on coordination of structure- and flow-oriented optimization. The model uncovers a practical approach to analyze and optimize supply chain redundancy by varying processing intensities of resource consumption in the network according to supply and structural dynamics. This makes it possible to explicitly include the dynamics of resource consumption for contingency plan realization in disruption scenarios.

Suggested Citation

  • Alexander Pavlov & Dmitry Ivanov & Dmitry Pavlov & Alexey Slinko, 2025. "Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics," Annals of Operations Research, Springer, vol. 349(2), pages 495-524, June.
    • Handle: RePEc:spr:annopr:v:349:y:2025:i:2:d:10.1007_s10479-019-03182-6
    DOI: 10.1007/s10479-019-03182-6
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    References listed on IDEAS

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