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Dynamical Supply Networks for Crisis and Disaster Relief: Networks Resilience and Decision Support in Uncertain Environments

In: Operations Research Proceedings 2013

Author

Listed:
  • Silja Meyer-Nieberg

    (Universität der Bundeswehr München)

  • Erik Kropat

    (Universität der Bundeswehr München)

  • Patrick Dolan Weber

    (University of Arizona)

Abstract

Recent natural disasters affected many parts of the world and resulted in an extensive loss of life and disruption of infrastructure. The randomness of impacts and the urgency of response efforts require a rapid decision making in an often uncertain and complex environment. In particular, the organization and controlling of efficient humanitarian supply chains are challenging the operational analyst from both the theoretical and practical perspective. A far-sighted and comprehensive emergency planning can alleviate the effects of sudden-onset disasters and facilitate the efficient delivery of required commodities and humanitarian aid to the victims. Methods from computational networks and agent-based modelling supported by sophisticated data farming experiments allow a detailed analysis of network performance measures and an evaluation of the vulnerability of infrastructure and supply networks. These approaches can be used for relief planning as well as for a simulation of continuous aid work threatened by severe disruptions. This paper presents a first step towards an integrated dynamic network optimization approach which combines forecasting models and simulation.

Suggested Citation

  • Silja Meyer-Nieberg & Erik Kropat & Patrick Dolan Weber, 2014. "Dynamical Supply Networks for Crisis and Disaster Relief: Networks Resilience and Decision Support in Uncertain Environments," Operations Research Proceedings, in: Dennis Huisman & Ilse Louwerse & Albert P.M. Wagelmans (ed.), Operations Research Proceedings 2013, edition 127, pages 309-315, Springer.
  • Handle: RePEc:spr:oprchp:978-3-319-07001-8_42
    DOI: 10.1007/978-3-319-07001-8_42
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    Cited by:

    1. Olfati, Marjan & Paydar, Mohammad Mahdi, 2023. "Towards a responsive-sustainable-resilient tea supply chain network design under uncertainty using big data," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Arabi, Mahsa & Gholamian, Mohammad Reza, 2023. "Resilient closed-loop supply chain network design considering quality uncertainty: A case study of stone quarries," Resources Policy, Elsevier, vol. 80(C).
    3. Mohammad Reza Seddigh & Sajjad Shokouhyar & Fatemeh Loghmani, 2023. "Approaching towards sustainable supply chain under the spotlight of business intelligence," Annals of Operations Research, Springer, vol. 324(1), pages 937-970, May.
    4. Yusuf Kuvvetli, 2023. "A goal programming model for two-stage COVID19 test sampling centers location-allocation problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 1-20, March.

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