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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.
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