IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v24y2022i2p766-790.html

A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory

Author

Listed:
  • Joline Uichanco

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Problem definition : We study the problem faced by the Philippine Department of Social Welfare (DSWD) in prepositioning relief items before landfall of an oncoming typhoon whose future outcome (trajectory and wind speed) is uncertain. Academic/practical relevance : The importance of prepositioning was a hard lesson learned from Super Typhoon Haiyan that devastated the Philippines in 2013, when many affected by the typhoon did not have immediate access to food and water. In a typhoon-prone country, it is important to build resilience through an effective prepositioning model. Methodology : By engaging with DSWD, we developed a practically relevant stochastic prepositioning model. The probability models of municipality-level demand and of supply damage are both dependent on the typhoon outcome. A linear mixed effects model is used to estimate the dependence of demand on the typhoon outcome using a large data set that includes the municipality-level impact of West Pacific typhoons during 2008–2019. The model has two objectives motivated from the practical realities of the Philippine network: prioritizing regions with high demand and prepositioning in all affected regions proportional to their total demand. Results : We find that the choice of the demand model significantly impacts the distributed relief items in the Philippine setting where it is challenging to adjust region-level supply after a typhoon. By using the historical data on past typhoons, we show that in this setting, our stochastic demand model provides the best distribution to date of any existing demand models. Managerial implications : There currently exists a gap between theory and practice in the management of relief inventories. We contribute toward bridging this gap by engaging with DSWD to develop a practically relevant relief distribution model. Our work is an effective example of collaboration with government and nongovernment agencies in developing a relief distribution model.

Suggested Citation

  • Joline Uichanco, 2022. "A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory," Manufacturing & Service Operations Management, INFORMS, vol. 24(2), pages 766-790, March.
    • Handle: RePEc:inm:ormsom:v:24:y:2022:i:2:p:766-790
    DOI: 10.1287/msom.2021.0980
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2021.0980
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2021.0980?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xing Hong & Miguel A. Lejeune & Nilay Noyan, 2015. "Stochastic network design for disaster preparedness," IISE Transactions, Taylor & Francis Journals, vol. 47(4), pages 329-357, April.
    2. Mete, Huseyin Onur & Zabinsky, Zelda B., 2010. "Stochastic optimization of medical supply location and distribution in disaster management," International Journal of Production Economics, Elsevier, vol. 126(1), pages 76-84, July.
    3. Consuelos Salas, Laura & Robles Cárdenas, Manuel & Zhang, Muhong, 2012. "Inventory policies for humanitarian aid during hurricanes," Socio-Economic Planning Sciences, Elsevier, vol. 46(4), pages 272-280.
    4. Davis, Lauren B. & Samanlioglu, Funda & Qu, Xiuli & Root, Sarah, 2013. "Inventory planning and coordination in disaster relief efforts," International Journal of Production Economics, Elsevier, vol. 141(2), pages 561-573.
    5. E J Lodree Jr & S Taskin, 2008. "An insurance risk management framework for disaster relief and supply chain disruption inventory planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(5), pages 674-684, May.
    6. S Taskin & E J Lodree,, 2011. "A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1098-1108, June.
    7. 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.
    8. 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.
    9. Nilay Noyan & Burcu Balcik & Semih Atakan, 2016. "A Stochastic Optimization Model for Designing Last Mile Relief Networks," Transportation Science, INFORMS, vol. 50(3), pages 1092-1113, August.
    10. Tofighi, S. & Torabi, S.A. & Mansouri, S.A., 2016. "Humanitarian logistics network design under mixed uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 239-250.
    11. Campbell, Ann Melissa & Jones, Philip C., 2011. "Prepositioning supplies in preparation for disasters," European Journal of Operational Research, Elsevier, vol. 209(2), pages 156-165, March.
    12. Kovacs, Gyöngyi & Moshtari, Mohammad, 2019. "A roadmap for higher research quality in humanitarian operations: A methodological perspective," European Journal of Operational Research, Elsevier, vol. 276(2), pages 395-408.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fardi, Keyvan & Ghasemzadeh, Fatemeh & Farahani, Reza Zanjirani & Asgari, Nasrin & Laker, Benjamin & Ruiz, Rubén, 2025. "The impact of frequency and magnitude of natural disasters on inventory prepositioning," European Journal of Operational Research, Elsevier, vol. 322(2), pages 511-540.
    2. Hongming Li & Erick Delage & Ning Zhu & Michael Pinedo & Shoufeng Ma, 2024. "Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations," Manufacturing & Service Operations Management, INFORMS, vol. 26(1), pages 197-214, January.
    3. Xabier Barriola & William Schmidt, 2025. "The High Impact of Hurricanes on Prices in Low-Income Communities," Manufacturing & Service Operations Management, INFORMS, vol. 27(4), pages 993-1007, July.
    4. Gao, Pan & Li, Min & Wu, Zhongming & Zhang, Zhenzhen, 2026. "Two-stage distributionally robust optimization approach for drone-supported facility location and post-disaster relief distribution," Omega, Elsevier, vol. 139(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Shaoqing Geng & Yu Gong & Hanping Hou & Jianliang Yang & Bhakti Stephan Onggo, 2024. "Resource management in disaster relief: a bibliometric and content-analysis-based literature review," Annals of Operations Research, Springer, vol. 343(1), pages 263-292, December.
    3. 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.
    4. Fardi, Keyvan & Ghasemzadeh, Fatemeh & Farahani, Reza Zanjirani & Asgari, Nasrin & Laker, Benjamin & Ruiz, Rubén, 2025. "The impact of frequency and magnitude of natural disasters on inventory prepositioning," European Journal of Operational Research, Elsevier, vol. 322(2), pages 511-540.
    5. Prima Denny Sentia & Syaimak Abdul Shukor & Amelia Natasya Abdul Wahab & Muriati Mukhtar, 2023. "Logistic distribution in humanitarian supply chain management: a thematic literature review and future research," Annals of Operations Research, Springer, vol. 323(1), pages 175-201, April.
    6. Renata Turkeš & Daniel Palhazi Cuervo & Kenneth Sörensen, 2019. "Pre-positioning of emergency supplies: does putting a price on human life help to save lives?," Annals of Operations Research, Springer, vol. 283(1), pages 865-895, December.
    7. 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.
    8. 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).
    9. Coskun, Abdullah & Salman, F. Sibel & Pashapour, Amirreza, 2024. "Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    10. Dönmez, Zehranaz & Kara, Bahar Y. & Karsu, Özlem & Saldanha-da-Gama, Francisco, 2021. "Humanitarian facility location under uncertainty: Critical review and future prospects," Omega, Elsevier, vol. 102(C).
    11. Renata Turkeš & Kenneth Sörensen & Daniel Palhazi Cuervo, 2021. "A matheuristic for the stochastic facility location problem," Journal of Heuristics, Springer, vol. 27(4), pages 649-694, August.
    12. Aghajani, Mojtaba & Ali Torabi, S. & Altay, Nezih, 2023. "Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption," Omega, Elsevier, vol. 118(C).
    13. Galindo, Gina & Batta, Rajan, 2013. "Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 20-37.
    14. Hani Zbib & Burcu Balcik & Marie-Ève Rancourt & Gilbert Laporte, 2024. "A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves," Operations Research, INFORMS, vol. 72(5), pages 2014-2041, September.
    15. A. Anaya-Arenas & J. Renaud & A. Ruiz, 2014. "Relief distribution networks: a systematic review," Annals of Operations Research, Springer, vol. 223(1), pages 53-79, December.
    16. Yu, Xinyao & Chen, Jie & Zhu, Ning & Ma, Shoufeng & Peng, Binbin, 2025. "A robust stochastic approach to relief pre-positioning for earthquake response under event-wise uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    17. Alem, Douglas & Clark, Alistair & Moreno, Alfredo, 2016. "Stochastic network models for logistics planning in disaster relief," European Journal of Operational Research, Elsevier, vol. 255(1), pages 187-206.
    18. Afshin Kamyabniya & M. M. Lotfi & Mohsen Naderpour & Yuehwern Yih, 2018. "Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach," Information Systems Frontiers, Springer, vol. 20(4), pages 759-782, August.
    19. 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.
    20. Ali Torabi, S. & Shokr, Iman & Tofighi, Saeideh & Heydari, Jafar, 2018. "Integrated relief pre-positioning and procurement planning in humanitarian supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 123-146.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:24:y:2022:i:2:p:766-790. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.