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Operations Research Proceedings 2014 pp 9–15Cite as

Stochastic Dynamic Programming Solution of a Risk-Adjusted Disaster Preparedness and Relief Distribution Problem

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Abstract

This chapter proposes a multistage stochastic optimization framework that dynamically updates the purchasing and distribution decisions of emergency commodities in the aftermath of an earthquake. Furthermore, the models consider the risk of exceeding the budget levels at any stage through chance constraints, which are then converted to Conditional Value-at-Risk constraints. Compared to the previous papers, our framework provides the flexibility of adjusting the level of conservativeness to the users by changing risk related parameters. Under some conditions, the resulting linear programming problems are solved through the Stochastic Dual Dynamic Programming algorithm. The preliminary numerical results are encouraging.

This research with the project number 13.402.005 has been financially supported by Galatasaray University Research Fund.

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References

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Authors and Affiliations

  1. Department of Industrial Engineering, Galatasaray University, Ciragan Cad. Ortaköy, 34349, Istanbul, Turkey

    Ebru Angün

Authors
  1. Ebru Angün
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Correspondence to Ebru Angün .

Editor information

Editors and Affiliations

  1. Operations Research, RWTH Aachen University, Aachen, Germany

    Marco Lübbecke

  2. Lehrstuhl II für Mathematik, RWTH Aachen University, Aachen, Germany

    Arie Koster

  3. RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Peter Letmathe

  4. E.ON Energy Research Center, RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Reinhard Madlener

  5. Management Science, RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Britta Peis

  6. Chair of Operations Management, RWTH Aachen University, Aachen, Germany

    Grit Walther

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Angün, E. (2016). Stochastic Dynamic Programming Solution of a Risk-Adjusted Disaster Preparedness and Relief Distribution Problem. In: Lübbecke, M., Koster, A., Letmathe, P., Madlener, R., Peis, B., Walther, G. (eds) Operations Research Proceedings 2014. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-28697-6_2

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