IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v62y2018icp84-103.html
   My bibliography  Save this article

Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution

Author

Listed:
  • Li, Bin
  • Hernandez, Ivan
  • Milburn, Ashlea Bennett
  • Ramirez-Marquez, Jose Emmanuel

Abstract

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios.

Suggested Citation

  • Li, Bin & Hernandez, Ivan & Milburn, Ashlea Bennett & Ramirez-Marquez, Jose Emmanuel, 2018. "Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 84-103.
  • Handle: RePEc:eee:soceps:v:62:y:2018:i:c:p:84-103
    DOI: 10.1016/j.seps.2017.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012116300908
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.seps.2017.09.003?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. de la Torre, Luis E. & Dolinskaya, Irina S. & Smilowitz, Karen R., 2012. "Disaster relief routing: Integrating research and practice," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 88-97.
    2. N Görmez & M Köksalan & F S Salman, 2011. "Locating disaster response facilities in Istanbul," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1239-1252, July.
    3. Rawls, Carmen G. & Turnquist, Mark A., 2010. "Pre-positioning of emergency supplies for disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 521-534, May.
    4. Owen, Susan Hesse & Daskin, Mark S., 1998. "Strategic facility location: A review," European Journal of Operational Research, Elsevier, vol. 111(3), pages 423-447, December.
    5. Wilfredo Yushimito & Miguel Jaller & Satish Ukkusuri, 2012. "A Voronoi-Based Heuristic Algorithm for Locating Distribution Centers in Disasters," Networks and Spatial Economics, Springer, vol. 12(1), pages 21-39, March.
    6. Widener, Michael J. & Horner, Mark W., 2011. "A hierarchical approach to modeling hurricane disaster relief goods distribution," Journal of Transport Geography, Elsevier, vol. 19(4), pages 821-828.
    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. Jang, Hoon & Lee, Jun-Ho, 2019. "A hierarchical location model for determining capacities of neonatal intensive care units in Korea," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    2. Sara Rye & Emel Aktas, 2023. "A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model," Logistics, MDPI, vol. 7(2), pages 1-24, May.
    3. Zobel, Christopher W. & Baghersad, Milad, 2020. "Analytically comparing disaster resilience across multiple dimensions," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    4. 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.
    5. 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).

    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. 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.
    2. 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.
    3. Chowdhury, Sudipta & Emelogu, Adindu & Marufuzzaman, Mohammad & Nurre, Sarah G. & Bian, Linkan, 2017. "Drones for disaster response and relief operations: A continuous approximation model," International Journal of Production Economics, Elsevier, vol. 188(C), pages 167-184.
    4. Ahmadi, Morteza & Seifi, Abbas & Tootooni, Behnam, 2015. "A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 145-163.
    5. Hasani, Aliakbar & Mokhtari, Hadi, 2018. "Redesign strategies of a comprehensive robust relief network for disaster management," Socio-Economic Planning Sciences, Elsevier, vol. 64(C), pages 92-102.
    6. Wang, Haijun & Du, Lijing & Ma, Shihua, 2014. "Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 160-179.
    7. Dilsu Binnaz Ozkapici & Mustafa Alp Ertem & Haluk Aygüneş, 2016. "Intermodal humanitarian logistics model based on maritime transportation in Istanbul," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 345-364, August.
    8. Kılcı, Fırat & Kara, Bahar Yetiş & Bozkaya, Burçin, 2015. "Locating temporary shelter areas after an earthquake: A case for Turkey," European Journal of Operational Research, Elsevier, vol. 243(1), pages 323-332.
    9. Hu, Shao-Long & Han, Chuan-Feng & Meng, Ling-Peng, 2016. "Stochastic optimization for investment in facilities in emergency prevention," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 14-31.
    10. 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.
    11. Aurelie Charles & Matthieu Lauras & Luk N. van Wassenhove & Lionel Dupont, 2016. "Designing an efficient humanitarian supply network," Post-Print hal-01532132, HAL.
    12. Acar, Müge & Kaya, Onur, 2019. "A healthcare network design model with mobile hospitals for disaster preparedness: A case study for Istanbul earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 273-292.
    13. Battarra, Maria & Balcik, Burcu & Xu, Huifu, 2018. "Disaster preparedness using risk-assessment methods from earthquake engineering," European Journal of Operational Research, Elsevier, vol. 269(2), pages 423-435.
    14. Agha Iqbal Ali & Guven Ince, 2017. "Distress among disaster-affected populations: delay in relief provision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 533-543, May.
    15. TURKEŠ, Renata & SÖRENSEN, Kenneth & PALHAZI CUERVO, Daniel, 2020. "Deriving rules of thumb for facility decision making in humanitarian operations," Working Papers 2020002, University of Antwerp, Faculty of Business and Economics.
    16. 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.
    17. Rennemo, Sigrid Johansen & Rø, Kristina Fougner & Hvattum, Lars Magnus & Tirado, Gregorio, 2014. "A three-stage stochastic facility routing model for disaster response planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 116-135.
    18. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    19. 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.
    20. Ali Ekici & Okan Örsan Özener, 2020. "Inventory routing for the last mile delivery of humanitarian relief supplies," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 621-660, September.

    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:eee:soceps:v:62:y:2018:i:c:p:84-103. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.