IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v35y2024i2p609-628.html
   My bibliography  Save this article

Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management

Author

Listed:
  • Daniel Suarez

    (Centro para la Optimización y la Probabilidad Aplicada, Departamento de Ingeniería Industrial, Universidad de los Andes, Bogotá 111711, Colombia)

  • Camilo Gomez

    (Centro para la Optimización y la Probabilidad Aplicada, Departamento de Ingeniería Industrial, Universidad de los Andes, Bogotá 111711, Colombia)

  • Andrés L. Medaglia

    (Centro para la Optimización y la Probabilidad Aplicada, Departamento de Ingeniería Industrial, Universidad de los Andes, Bogotá 111711, Colombia)

  • Raha Akhavan-Tabatabaei

    (Sabanci Business School, Sabanci University, Istambul 34956, Turkey)

  • Sthefania Grajales

    (INGENIAR: Risk Intelligence, Bogotá 110221, Colombia; Escola de Camins, Universitat Politècnica de Catalunya – UPC, 08034 Barcelona, Spain)

Abstract

A central challenge in disaster risk management (DRM) is that there are key dependencies and uncertainty between the decisions made at the mitigation, preparedness, response, and recovery stages. Evaluating the impact of strategic decisions on the decisions and outcomes of subsequent stages is paramount to determine informed risk management policies (e.g., estimating the risk reduction that may be achieved by a mitigation strategy relative to its cost). Performing such analyses is difficult, not only because of the uncertainty inherent to disastrous events but because it implies integrating the logic and data of different processes that occur at each stage. Comprehensive decision support systems for disaster management and thus require information systems that allow timely and reliable integration of data sources from different domains, including information on hazards and vulnerabilities for risk analysis and organizational and logistical information for decision analysis. We propose an analytics-centered framework that integrates predictive and prescriptive models responding to unique characteristics of DRM. The framework relies on probabilistic risk assessment and uses optimization-based simulation of the response phase as a means to inform decisions at the preparedness stage. This paper presents a case study regarding the analysis of preparedness and response decisions for wildfire control in Uruguay. Numerical results illustrate the insights that can be derived from the integration of data and models at multiple stages. Specifically, in the Uruguay case, slight reductions in the preparedness budget can lead to disproportionate losses during the response stage, whereas slight increases have little effect unless explicitly directed to control high-consequence scenarios. Motivated by a real-world problem, this case study emphasizes the challenges for integrated information systems that enable the potential of analytical decision support frameworks for DRM.

Suggested Citation

  • Daniel Suarez & Camilo Gomez & Andrés L. Medaglia & Raha Akhavan-Tabatabaei & Sthefania Grajales, 2024. "Integrated Decision Support for Disaster Risk Management: Aiding Preparedness and Response Decisions in Wildfire Management," Information Systems Research, INFORMS, vol. 35(2), pages 609-628, June.
  • Handle: RePEc:inm:orisre:v:35:y:2024:i:2:p:609-628
    DOI: 10.1287/isre.2022.0118
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2022.0118
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2022.0118?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. Laura Steinberg & Hatice Sengul & Ana Cruz, 2008. "Natech risk and management: an assessment of the state of the art," 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. 46(2), pages 143-152, August.
    2. Mikael Rönnqvist & Sophie D’Amours & Andres Weintraub & Alejandro Jofre & Eldon Gunn & Robert Haight & David Martell & Alan Murray & Carlos Romero, 2015. "Operations Research challenges in forestry: 33 open problems," Annals of Operations Research, Springer, vol. 232(1), pages 11-40, September.
    3. João António Zeferino, 2020. "Optimizing the location of aerial resources to combat wildfires: a case study of Portugal," 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. 100(3), pages 1195-1213, February.
    4. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
    5. Camilo Gomez & Andrés D. González & Hiba Baroud & Claudia D. Bedoya‐Motta, 2019. "Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1913-1929, September.
    6. David Alexander, 2014. "Communicating earthquake risk to the public: the trial of the “L’Aquila Seven”," 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. 72(2), pages 1159-1173, June.
    7. Stefan Seidel & Leona Chandra Kruse & Nadine Székely & Michael Gau & Daniel Stieger & Ken Peffers & Tuure Tuunanen & Björn Niehaves & Kalle Lyytinen, 2018. "Design principles for sensemaking support systems in environmental sustainability transformations," European Journal of Information Systems, Taylor & Francis Journals, vol. 27(2), pages 221-247, March.
    8. Lili Yang & Guofeng Su & Hongyong Yuan, 2012. "Design Principles of Integrated Information Platform for Emergency Responses: The Case of 2008 Beijing Olympic Games," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 761-786, September.
    9. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    10. Galindo, Gina & Batta, Rajan, 2013. "Review of recent developments in OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 230(2), pages 201-211.
    11. Mavsar, Robert & González Cabán, Armando & Varela, Elsa, 2013. "The state of development of fire management decision support systems in America and Europe," Forest Policy and Economics, Elsevier, vol. 29(C), pages 45-55.
    12. Robin L. Dillon & M. Elisabeth Paté-Cornell & Seth D. Guikema, 2003. "Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints," Operations Research, INFORMS, vol. 51(3), pages 354-370, June.
    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. Ahmed Abbasi & Jeffrey Parsons & Gautam Pant & Olivia R. Liu Sheng & Suprateek Sarker, 2024. "Pathways for Design Research on Artificial Intelligence," Information Systems Research, INFORMS, vol. 35(2), pages 441-459, June.
    2. Ahmed Abbasi & Robin Dillon & H. Raghav Rao & Olivia R. Liu Sheng, 2024. "Preparedness and Response in the Century of Disasters: Overview of Information Systems Research Frontiers," Information Systems Research, INFORMS, vol. 35(2), pages 460-468, June.

    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. Araya-Córdova, P.J. & Vásquez, Óscar C., 2018. "The disaster emergency unit scheduling problem to control wildfires," International Journal of Production Economics, Elsevier, vol. 200(C), pages 311-317.
    2. 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.
    3. Lu, Chung-Cheng & Ying, Kuo-Ching & Chen, Hui-Ju, 2016. "Real-time relief distribution in the aftermath of disasters – A rolling horizon approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 1-20.
    4. Melissa Gama & Bruno Filipe Santos & Maria Paola Scaparra, 2016. "A multi-period shelter location-allocation model with evacuation orders for flood disasters," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 4(3), pages 299-323, September.
    5. Sperling, Martina & Schryen, Guido, 2022. "Decision support for disaster relief: Coordinating spontaneous volunteers," European Journal of Operational Research, Elsevier, vol. 299(2), pages 690-705.
    6. Diaz, Rafael & Behr, Joshua G. & Acero, Beatriz, 2022. "Coastal housing recovery in a postdisaster environment: A supply chain perspective," International Journal of Production Economics, Elsevier, vol. 247(C).
    7. Laijun Zhao & Huiyong Li & Yan Sun & Rongbing Huang & Qingmi Hu & Jiajia Wang & Fei Gao, 2017. "Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    8. Rivera, Juan Carlos & Murat Afsar, H. & Prins, Christian, 2016. "Mathematical formulations and exact algorithm for the multitrip cumulative capacitated single-vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 249(1), pages 93-104.
    9. Aurelie Charles & Matthieu Lauras & Luk N. van Wassenhove & Lionel Dupont, 2016. "Designing an efficient humanitarian supply network," Post-Print hal-01532132, HAL.
    10. Camilo Gomez & Andrés D. González & Hiba Baroud & Claudia D. Bedoya‐Motta, 2019. "Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1913-1929, September.
    11. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    12. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    13. Qi, Mingyao & Yang, Ying & Cheng, Chun, 2023. "Location and inventory pre-positioning problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    14. Deepa Mishra & Sameer Kumar & Elkafi Hassini, 2019. "Current trends in disaster management simulation modelling research," Annals of Operations Research, Springer, vol. 283(1), pages 1387-1411, December.
    15. 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.
    16. 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.
    17. Stienen, V.F. & Wagenaar, J.C. & den Hertog, D. & Fleuren, H.A., 2021. "Optimal depot locations for humanitarian logistics service providers using robust optimization," Omega, Elsevier, vol. 104(C).
    18. Kınay, Ömer Burak & Yetis Kara, Bahar & Saldanha-da-Gama, Francisco & Correia, Isabel, 2018. "Modeling the shelter site location problem using chance constraints: A case study for Istanbul," European Journal of Operational Research, Elsevier, vol. 270(1), pages 132-145.
    19. Samuel Fosso Wamba, 2022. "Humanitarian supply chain: a bibliometric analysis and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 937-963, December.
    20. Julia Monzón & Federico Liberatore & Begoña Vitoriano, 2020. "A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification," Mathematics, MDPI, vol. 8(4), pages 1-17, April.

    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:orisre:v:35:y:2024:i:2:p:609-628. 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.