IDEAS home Printed from https://ideas.repec.org/a/sae/joudef/v18y2021i2p135-156.html
   My bibliography  Save this article

Assessing uncertainty and risk in an expeditionary military logistics network

Author

Listed:
  • Brandon M McConnell
  • Thom J Hodgson
  • Michael G Kay
  • Russell E King
  • Yunan Liu
  • Greg H Parlier
  • Kristin Thoney-Barletta
  • James R Wilson

Abstract

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.

Suggested Citation

  • Brandon M McConnell & Thom J Hodgson & Michael G Kay & Russell E King & Yunan Liu & Greg H Parlier & Kristin Thoney-Barletta & James R Wilson, 2021. "Assessing uncertainty and risk in an expeditionary military logistics network," The Journal of Defense Modeling and Simulation, , vol. 18(2), pages 135-156, April.
    • Handle: RePEc:sae:joudef:v:18:y:2021:i:2:p:135-156
    DOI: 10.1177/1548512919860595
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1548512919860595
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1548512919860595?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. Izady, Navid & Worthington, Dave, 2012. "Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments," European Journal of Operational Research, Elsevier, vol. 219(3), pages 531-540.
    2. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry, 2005. "Author's response to Koehler and Gardner," International Journal of Forecasting, Elsevier, vol. 21(3), pages 619-620.
    3. Greg H. Parlier, 2016. "Mission-Based Forecasting: Demand Forecasting for Military Operations," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 43, pages 32-37, Fall.
    4. Noah Gans & Ger Koole & Avishai Mandelbaum, 2003. "Telephone Call Centers: Tutorial, Review, and Research Prospects," Manufacturing & Service Operations Management, INFORMS, vol. 5(2), pages 79-141, September.
    5. Song-Hee Kim & Ward Whitt, 2014. "Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes?," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 464-480, July.
    6. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    7. David L. Alderson & Gerald G. Brown & W. Matthew Carlyle, 2015. "Operational Models of Infrastructure Resilience," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 562-586, April.
    8. Thom J. Hodgson & Denis Cormier & Alexander J. Weintraub & Andrew Zozom, 1998. "Note. Satisfying Due Dates in Large Job Shops," Management Science, INFORMS, vol. 44(10), pages 1442-1446, October.
    9. Yunan Liu & Ward Whitt, 2012. "Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals," Operations Research, INFORMS, vol. 60(6), pages 1551-1564, December.
    10. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    11. Chen, Xiaoming & Zhou, Xuesong & List, George F., 2011. "Using time-varying tolls to optimize truck arrivals at ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 965-982.
    Full references (including those not matched with items on IDEAS)

    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. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    2. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, August.
    3. Schwarz, Justus Arne & Selinka, Gregor & Stolletz, Raik, 2016. "Performance analysis of time-dependent queueing systems: Survey and classification," Omega, Elsevier, vol. 63(C), pages 170-189.
    4. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    5. Xi Chen & Dave Worthington, 2017. "Staffing of time-varying queues using a geometric discrete time modelling approach," Annals of Operations Research, Springer, vol. 252(1), pages 63-84, May.
    6. Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Van Houdt, Benny, 2018. "Inpatient boarding in emergency departments: Impact on patient delays and system capacity," European Journal of Operational Research, Elsevier, vol. 271(3), pages 953-967.
    7. Mustafa Y. Sir & David Nestler & Thomas Hellmich & Devashish Das & Michael J. Laughlin & Michon C. Dohlman & Kalyan Pasupathy, 2017. "Optimization of Multidisciplinary Staffing Improves Patient Experiences at the Mayo Clinic," Interfaces, INFORMS, vol. 47(5), pages 425-441, October.
    8. Britt W. J. Mathijsen & A. J. E. M. Janssen & Johan S. H. Leeuwaarden & Bert Zwart, 2018. "Robust heavy-traffic approximations for service systems facing overdispersed demand," Queueing Systems: Theory and Applications, Springer, vol. 90(3), pages 257-289, December.
    9. Smirnov, Dmitry & Huchzermeier, Arnd, 2020. "Analytics for labor planning in systems with load-dependent service times," European Journal of Operational Research, Elsevier, vol. 287(2), pages 668-681.
    10. Cho, David D. & Stauffer, Jon M., 2022. "Tele-medicine question response service: Analysis of benefits and costs," Omega, Elsevier, vol. 111(C).
    11. Ward Whitt & Jingtong Zhao, 2017. "Many‐server loss models with non‐poisson time‐varying arrivals," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(3), pages 177-202, April.
    12. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    13. Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
    14. Carri W. Chan & Jing Dong & Linda V. Green, 2017. "Queues with Time-Varying Arrivals and Inspections with Applications to Hospital Discharge Policies," Operations Research, INFORMS, vol. 65(2), pages 469-495, April.
    15. Eugene Furman & Adam Diamant & Murat Kristal, 2021. "Customer Acquisition and Retention: A Fluid Approach for Staffing," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4236-4257, November.
    16. Notz, Pascal M. & Wolf, Peter K. & Pibernik, Richard, 2023. "Prescriptive analytics for a multi-shift staffing problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 887-901.
    17. Ran Liu & Michael E. Kuhl & Yunan Liu & James R. Wilson, 2019. "Modeling and Simulation of Nonstationary Non-Poisson Arrival Processes," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 347-366, April.
    18. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    19. Barış Ata & Xiaoshan Peng, 2020. "An Optimal Callback Policy for General Arrival Processes: A Pathwise Analysis," Operations Research, INFORMS, vol. 68(2), pages 327-347, March.
    20. Sergey Stepanov & Mikhail Stepanov, 2021. "Estimation of the Performance Measures of a Group of Servers Taking into Account Blocking and Call Repetition before and after Server Occupation," Mathematics, MDPI, vol. 9(21), pages 1-24, November.

    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:sae:joudef:v:18:y:2021:i:2:p:135-156. 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: SAGE Publications (email available below). General contact details of provider: .

    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.