Abstract
We study a last mile distribution network design problem for situations where there exist local distribution centers (LDCs) with prepositioned supplies. Given the information on the existing pre-disaster relief network, the problem determines the locations and capacities of LDCs and points of distribution in the relief network, while capturing the uncertain aspects of the post-disaster environment. We introduce a new accessibility metric and develop a two-stage stochastic programming model that would allow more accessible and equitable distribution of relief supplies. Since solving the proposed stochastic optimization model is computationally challenging, we employ a scenario decomposition-based branch-and-cut algorithm. We perform a computational study—based on the real-world data from the 2011 Van earthquake in Turkey—to provide insights about the model and demonstrate the effectiveness of the solution method.
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Acknowledgements
We thank Semih Atakan from the University of Southern California for his valuable advice on the implementation of the solution method. We thank Burcu Balcik and Gabor Rudolf for their valuable comments. The first author was supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK) Career Grant #111M543, and the second author was partially supported by the TUBITAK BIDEB program. We also thank the anonymous referees for their valuable feedback.
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Appendix A: Online data file
Appendix A: Online data file
The data related to our case study are available onlineFootnote 1 in the file titled “OnlineAppendix_ThreeEchelon.xls.” The file includes the list of 94 settlements in the main district of Van, the travel times between all settlements, the demographical information used to calculate the mobility scores associated with each demand point, mobility scores, risk scores, and cluster assignments. It also provides the base demands and accessibility scores for each test network.
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Noyan, N., Kahvecioğlu, G. Stochastic last mile relief network design with resource reallocation. OR Spectrum 40, 187–231 (2018). https://doi.org/10.1007/s00291-017-0498-7
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DOI: https://doi.org/10.1007/s00291-017-0498-7