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Optimizing Military Capital Planning

Author

Listed:
  • Gerald G. Brown

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Robert F. Dell

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Alexandra M. Newman

    (Division of Economics and Business, Colorado School of Mines, Golden, Colorado 80401)

Abstract

Planning United States military procurement commits a significant portion of our nation's wealth and determines our ability to defend ourselves, our allies, and our principles over the long term. Our military pioneered and has long used mathematical optimization to unravel the distinguishing complexities of military capital planning. The succession of mathematical optimization models we present exhibits increasingly detailed features; such embellishments are always needed for real-world, long-term procurement decision models. Two case studies illustrate practical modeling tricks that are useful in helping decision makers decide how to spend about a trillion dollars.

Suggested Citation

  • Gerald G. Brown & Robert F. Dell & Alexandra M. Newman, 2004. "Optimizing Military Capital Planning," Interfaces, INFORMS, vol. 34(6), pages 415-425, December.
    • Handle: RePEc:inm:orinte:v:34:y:2004:i:6:p:415-425
    DOI: 10.1287/inte.1040.0107
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    References listed on IDEAS

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    Cited by:

    1. Rempel, M. & Cai, J., 2021. "A review of approximate dynamic programming applications within military operations research," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Kangaspunta, Jussi & Liesiö, Juuso & Salo, Ahti, 2012. "Cost-efficiency analysis of weapon system portfolios," European Journal of Operational Research, Elsevier, vol. 223(1), pages 264-275.
    3. Andrew Lim & Fan Wang & Zhou Xu, 2006. "A Transportation Problem with Minimum Quantity Commitment," Transportation Science, INFORMS, vol. 40(1), pages 117-129, February.
    4. 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.
    5. Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
    6. Gerald G. Brown & Richard E. Rosenthal, 2008. "Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support," Interfaces, INFORMS, vol. 38(5), pages 356-366, October.
    7. Liesiö, Juuso & Punkka, Antti, 2014. "Baseline value specification and sensitivity analysis in multiattribute project portfolio selection," European Journal of Operational Research, Elsevier, vol. 237(3), pages 946-956.
    8. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    9. Hunkar Toyoglu & Oya Ekin Karasan & Bahar Yetis Kara, 2011. "Distribution network design on the battlefield," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(3), pages 188-209, April.
    10. Alexander J. Zolan & Michael S. Scioletti & David P. Morton & Alexandra M. Newman, 2021. "Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1300-1319, October.
    11. Phillips, Lawrence D. & Bana e Costa, Carlos A., 2005. "Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing," LSE Research Online Documents on Economics 22742, London School of Economics and Political Science, LSE Library.
    12. Lawrence Phillips & Carlos Bana e Costa, 2007. "Transparent prioritisation, budgeting and resource allocation with multi-criteria decision analysis and decision conferencing," Annals of Operations Research, Springer, vol. 154(1), pages 51-68, October.

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