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A Scenario Planning Approach for Shelter Location and Evacuation Routing

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Optimization and Decision Science: Methodologies and Applications (ODS 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 217))

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Abstract

Emergency planning operations are one of the key aspects of Disaster Operations Management (DOM) [1]. This work presents a scenario-based location-allocation-routing model to optimize evacuation planning decisions, including where to establish shelter sites and which routes to arrange to reach them, across different network disruption scenarios. The model considers both supported-evacuation and self-evacuation. The objective is to minimize the duration of the supported-evacuation while guaranteeing that the routes of self-evacuees do not exceed a given traveling time threshold. Both shelter location and routing decisions are optimized so as to identify solutions which perform well across different disruption scenarios. A mathematical formulation of this model is provided, which can be solved through a general-purpose solver optimization package for modest size instances. Some computational results are reported.

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Author information

Authors and Affiliations

  1. Kent Business School, University of Kent, Canterbury, Kent, UK

    Annunziata Esposito Amideo & Maria Paola Scaparra

Authors
  1. Annunziata Esposito Amideo
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  2. Maria Paola Scaparra
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Correspondence to Annunziata Esposito Amideo .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University “Federico II” of Naples, Naples, Italy

    Antonio Sforza

  2. Department of Electrical Engineering and Information Technology, University “Federico II” of Naples, Naples, Italy

    Claudio Sterle

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Esposito Amideo, A., Scaparra, M.P. (2017). A Scenario Planning Approach for Shelter Location and Evacuation Routing. In: Sforza, A., Sterle, C. (eds) Optimization and Decision Science: Methodologies and Applications. ODS 2017. Springer Proceedings in Mathematics & Statistics, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-67308-0_57

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