IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v48y2016i3p220-234.html
   My bibliography  Save this article

Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain

Author

Listed:
  • Hugh R. Medal
  • Edward A. Pohl
  • Manuel D. Rossetti

Abstract

We study a new facility protection problem in which one must allocate scarce protection resources to a set of facilities given that allocating resources to a facility only has a probabilistic effect on the facility’s post-disruption capacity. This study seeks to test three common assumptions made in the literature on modeling infrastructure systems subject to disruptions: 1) perfect protection, e.g., protecting an element makes it fail-proof, 2) binary protection, i.e., an element is either fully protected or unprotected, and 3) binary state, i.e., disrupted elements are fully operational or non-operational. We model this facility protection problem as a two-stage stochastic program with endogenous uncertainty. Because this stochastic program is non-convex we present a greedy algorithm and show that it has a worst-case performance of 0.63. However, empirical results indicate that the average performance is much better. In addition, experimental results indicate that the mean-value version of this model, in which parameters are set to their mean values, performs close to optimal. Results also indicate that the perfect and binary protection assumptions together significantly affect the performance of a model. On the other hand, the binary state assumption was found to have a smaller effect.

Suggested Citation

  • Hugh R. Medal & Edward A. Pohl & Manuel D. Rossetti, 2016. "Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain," IISE Transactions, Taylor & Francis Journals, vol. 48(3), pages 220-234, March.
    • Handle: RePEc:taf:uiiexx:v:48:y:2016:i:3:p:220-234
    DOI: 10.1080/0740817X.2015.1078013
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0740817X.2015.1078013
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0740817X.2015.1078013?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.

    Citations

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


    Cited by:

    1. Bhuiyan, Tanveer Hossain & Medal, Hugh R. & Harun, Sarah, 2020. "A stochastic programming model with endogenous and exogenous uncertainty for reliable network design under random disruption," European Journal of Operational Research, Elsevier, vol. 285(2), pages 670-694.
    2. Wu, Di & Liu, Xiang-dong & Yan, Xiang-bin & Peng, Rui & Li, Gang, 2019. "Equilibrium analysis of bitcoin block withholding attack: A generalized model," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 318-328.
    3. Li, Qing & Li, Mingchu & Tian, Yuan & Gan, Jianyuan, 2023. "A risk-averse tri-level stochastic model for locating and recovering facilities against attacks in an uncertain environment," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Tezcan, Barış & Maass, Kayse Lee, 2023. "Human trafficking interdiction with decision dependent success," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    5. Bhuiyan, Tanveer Hossain & Moseley, Maxwell C. & Medal, Hugh R. & Rashidi, Eghbal & Grala, Robert K., 2019. "A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior," European Journal of Operational Research, Elsevier, vol. 277(2), pages 699-718.
    6. Zhou, Rui & Bhuiyan, Tanveer Hossain & Medal, Hugh R. & Sherwin, Michael D. & Yang, Dong, 2022. "A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk," Omega, Elsevier, vol. 107(C).

    More about this item

    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:taf:uiiexx:v:48:y:2016:i:3:p:220-234. 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.

    We have no bibliographic references for this item. You can help adding them by using 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 Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.