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Forecast-driven model for prepositioning supplies in preparation for a foreseen hurricane

Author

Listed:
  • Gina Galindo Pacheco

    (University at Buffalo (SUNY), Buffalo, USA
    Universidad del Norte, Barranquilla, Colombia)

  • Rajan Batta

    (University at Buffalo (SUNY), Buffalo, USA)

Abstract

In this paper, we present a forecast-driven dynamic model for prepositioning relief items in preparation for a foreseen hurricane. Our model uses forecast advisories issued by the National Hurricane Center (NHC), which are issued every 6 h. Every time a new advisory is issued with updated information, our model determines the amount and location of units to be prepositioned and it also re-prepositions already prepositioned units. The model also determines the best time for starting the prepositioning activities. Our approach uses a combination of Decision Theory and stochastic programming. The outcomes of our model are presented in a way that could be easily understood by humanitarian practitioners who are ultimately the ones who would use and apply our model.

Suggested Citation

  • Gina Galindo Pacheco & Rajan Batta, 2016. "Forecast-driven model for prepositioning supplies in preparation for a foreseen hurricane," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(1), pages 98-113, January.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:1:p:98-113
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    Citations

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    Cited by:

    1. Jon M. Stauffer & Subodha Kumar, 2021. "Impact of Incorporating Returns into Pre‐Disaster Deployments for Rapid‐Onset Predictable Disasters," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 451-474, February.
    2. Sabbaghtorkan, Monir & Batta, Rajan & He, Qing, 2020. "Prepositioning of assets and supplies in disaster operations management: Review and research gap identification," European Journal of Operational Research, Elsevier, vol. 284(1), pages 1-19.
    3. Edwards, Dominiqueca R. & Idoko, Faith O. & Vogiatzis, Chrysafis & Davis, Lauren B. & Mirchandani, Pitu, 2023. "Determining optimal fuel delivery strategies under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    4. Aaron B. Hoskins & Hugh R. Medal, 2019. "Stochastic programming solution for placement of satellite ground stations," Annals of Operations Research, Springer, vol. 283(1), pages 267-288, December.
    5. Rezapour, Shabnam & Farahani, Reza Zanjirani & Morshedlou, Nazanin, 2021. "Impact of timing in post-warning prepositioning decisions on performance measures of disaster management: A real-life application," European Journal of Operational Research, Elsevier, vol. 293(1), pages 312-335.
    6. Paul, Jomon A. & Zhang, Minjiao, 2019. "Supply location and transportation planning for hurricanes: A two-stage stochastic programming framework," European Journal of Operational Research, Elsevier, vol. 274(1), pages 108-125.
    7. Li, Xiaoping & Batta, Rajan & Kwon, Changhyun, 2017. "Effective and equitable supply of gasoline to impacted areas in the aftermath of a natural disaster," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 25-34.
    8. Shiripour, Saber & Mahdavi-Amiri, Nezam, 2019. "Optimal distribution of the injured in a multi-type transportation network with damage-dependent travel times: Two metaheuristic approaches," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    9. Acar, Müge & Kaya, Onur, 2022. "Inventory decisions for humanitarian aid materials considering budget constraints," European Journal of Operational Research, Elsevier, vol. 300(1), pages 95-111.
    10. Wang, Jing & Cai, Jianping & Yue, Xiaohang & Suresh, Nallan C., 2021. "Pre-positioning and real-time disaster response operations: Optimization with mobile phone location data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    11. Jose Escribano Macias & Nils Goldbeck & Pei-Yuan Hsu & Panagiotis Angeloudis & Washington Ochieng, 2020. "Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(4), pages 1089-1125, December.
    12. Yanbin Chang & Yongjia Song & Burak Eksioglu, 2022. "A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty," Annals of Operations Research, Springer, vol. 319(1), pages 1231-1263, December.
    13. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.
    14. Yusen Ye & Wen Jiao & Hong Yan, 2020. "Managing Relief Inventories Responding to Natural Disasters: Gaps Between Practice and Literature," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 807-832, April.
    15. Rodríguez-Espíndola, Oscar & Ahmadi, Hossein & Gastélum-Chavira, Diego & Ahumada-Valenzuela, Omar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel, 2023. "Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    16. Zhang, Yuwei & Li, Zhenping & Zhao, Yuwei, 2023. "Multi-mitigation strategies in medical supplies for epidemic outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

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