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Selective Routing for Post-disaster Needs Assessments

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Dynamics of Disasters—Key Concepts, Models, Algorithms, and Insights (DOD 2015 2016)

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Abstract

In the immediate aftermath of a disaster, relief agencies perform needs assessment operations to investigate the effects of the disaster and understand the needs of the affected communities. Since assessments must be performed quickly, it may not be possible to visit each site in the affected region. In practice, sites to be visited during the assessment period are selected considering the characteristics of the target communities. In this study, we address site selection and routing decisions of the rapid needs assessment teams that aim to evaluate the post-disaster conditions of a diverse set of community groups with different characteristics (e.g., ethnicity, income level, etc.) within a limited period of time. In particular, we study the Selective Assessment Routing Problem (SARP) that determines sites to be visited and the order of site visits for each team while ensuring sufficient coverage of the given set of characteristics. We present a mathematical model and greedy heuristics for the SARP. We perform numerical analysis to evaluate the performance of the greedy heuristics and show that the heuristic version that balances the tradeoff between coverage and travel times provides reasonable solutions for realistic problem instances.

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Acknowledgements

This work was presented at the 2nd International Conference on Dynamics of Disasters, Kalamata, Greece, June 29–July 2, 2015. This research has been funded by the Scientific and Technological Research Council of Turkey (TUBITAK) Career Award [213M414]. The author would like to thank Ilknur Singin, Alperen Talaslioglu, Busra Uydasoglu, Burak Guragac, and Yasin Dogan for their help with various phases of this ongoing project. The author would also thank to the Science Academy of Turkey for the BAGEP research award.

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Authors and Affiliations

  1. Industrial Engineering Department, Ozyegin University, Istanbul, Turkey

    Burcu Balcik

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  1. Burcu Balcik
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Correspondence to Burcu Balcik .

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Editors and Affiliations

  1. Department of Physics and Computer Science, Wilfrid Laurier University, Waterloo, Ontario, Canada

    Ilias S. Kotsireas

  2. Department of Operations and Information Management, University of Massachusetts, Amherst, Massachusetts, USA

    Anna Nagurney

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

Appendices

Appendix 1

The pseudocode for the GHa is provided in Algorithm 1. The pseudocodes for GHb and GHc are not provided as they are the same as Algorithm 1 except the definition of the set \(\mathcal{A}\). Specifically, to adapt the following pseudocode for the GHb, one can define set \(\mathcal{A}\) as the characteristic(s) with the smallest value of r c . Finally, in GHc, set \(\mathcal{A}\) includes all characteristics that have the smallest or the second smallest values of r c .

Algorithm 1 Greedy Heuristic (GHa)

Appendix 2

Example networks and characteristic matrices are presented in Fig. 3 and Tables 2 and 3.

Fig. 3
figure 3

Example 25 node networks used in problem instances. (a ) 25-node R-type network. (b ) 25-node RC-type network

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Table 2 Node characteristics (α ic values) for the 25 node R type network
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Table 3 Node characteristics (α ic values) for the 25 node RC type network
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Balcik, B. (2016). Selective Routing for Post-disaster Needs Assessments. In: Kotsireas, I., Nagurney, A., Pardalos, P. (eds) Dynamics of Disasters—Key Concepts, Models, Algorithms, and Insights. DOD 2015 2016. Springer Proceedings in Mathematics & Statistics, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-319-43709-5_2

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