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Humanitarian relief chain: Rapid response under uncertainty

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  • Chakravarty, Amiya K.

Abstract

The recent worldwide devastations have reemphasized the importance of rapid response for saving life. Relief supplies must arrive on time and in adequate quantities. Coordination in a relief chain is complex because of the uncertainties associated with disaster intensity, strike probability, infrastructure-disruption, and the actual damage. The relief effort must weigh the expected social value against delivery delays, and costs of logistics. We explore a 2-stage proactive/reactive approach where response time and relief amounts are decided ex post (after disaster occurs), and the prepositioned inventory is determined ex ante (before disaster occurs). Our major findings are: (i) the response quantity and time must be adjusted to imputed social value, but differently at different disaster intensities and cost structures, and in different communities, (ii) disaster intensity can be categorized into ranges that reveal whether rapid response and/or large relief quantity would be appropriate in each range, (iii) effectiveness of the relief strategy decreases as the disaster intensity increases, and (iv) in scenarios with limited budget, it is possible to earmark a unique budget for real time relief operations, that ensures maximization of social value.

Suggested Citation

  • Chakravarty, Amiya K., 2014. "Humanitarian relief chain: Rapid response under uncertainty," International Journal of Production Economics, Elsevier, vol. 151(C), pages 146-157.
  • Handle: RePEc:eee:proeco:v:151:y:2014:i:c:p:146-157
    DOI: 10.1016/j.ijpe.2013.10.007
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    11. V. G. Venkatesh & Abraham Zhang & Eric Deakins & Sunil Luthra & S. Mangla, 2019. "A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains," Annals of Operations Research, Springer, vol. 283(1), pages 1517-1550, December.
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    13. Amiya K. Chakravarty, 2018. "Humanitarian response to hurricane disasters: Coordinating flood‐risk mitigation with fundraising and relief operations," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(3), pages 275-288, April.
    14. Rivera-Royero, Daniel & Galindo, Gina & Yie-Pinedo, Ruben, 2016. "A dynamic model for disaster response considering prioritized demand points," Socio-Economic Planning Sciences, Elsevier, vol. 55(C), pages 59-75.
    15. Patra, T. Devi Prasad & Jha, J.K., 2021. "A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    16. Boutselis, Petros & McNaught, Ken, 2014. "Finite-Time Horizon Logistics Decision Making Problems: Consideration of a Wider Set of Factors," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Innovative Methods in Logistics and Supply Chain Management: Current Issues and Emerging Practices. Proceedings of the Hamburg International Conferenc, volume 19, pages 249-274, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
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    18. Baharmand, Hossein & Comes, Tina & Lauras, Matthieu, 2019. "Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 86-110.
    19. Yin, Yunqiang & Xu, Xinrui & Wang, Dujuan & Yu, Yugang & Cheng, T.C.E., 2024. "Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem," Transportation Research Part B: Methodological, Elsevier, vol. 182(C).
    20. Peiyu Zhang & Yankui Liu & Guoqing Yang & Guoqing Zhang, 2022. "A multi-objective distributionally robust model for sustainable last mile relief network design problem," Annals of Operations Research, Springer, vol. 309(2), pages 689-730, February.

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