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The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions

Author

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  • Nathaniel D. Bastian

    (Center for Integrated Healthcare Delivery Systems, Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania 16802; and Center for AMEDD Strategic Studies, U.S. Army Medical Department Center and School, Fort Sam Houston, Texas 78234)

  • Pat McMurry

    (AMEDD Personnel Proponency Directorate, U.S. Army Medical Department Center and School, Fort Sam Houston, Texas 78234)

  • Lawrence V. Fulton

    (Center for Healthcare Innovation, Education and Research, Rawls College of Business Administration, Texas Tech University, Lubbock, Texas 79410)

  • Paul M. Griffin

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Shisheng Cui

    (Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Thor Hanson

    (Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Sharan Srinivas

    (Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania 16802)

Abstract

The mission of the Army Medical Department (AMEDD) is to provide medical and healthcare delivery for the U.S. Army. Given the large number of medical specialties in the AMEDD, determining the appropriate number of hires and promotions for each medical specialty is a complex task. The AMEDD Personnel Proponency Directorate (APPD) previously used a manual approach to project the number of hires, promotions, and personnel inventory for each medical specialty across the AMEDD to support a 30-year life cycle. As a means of decision support to APPD, we proffer the objective force model (OFM) to optimize AMEDD workforce planning. We also employ a discrete-event simulation model to verify and validate the results.In this paper, we describe the OFM applied to the Medical Specialist Corps, one of the six officer corps in the AMEDD. The OFM permits better transparency of personnel for senior AMEDD decision makers, whereas effectively projecting the optimal number of officers to meet the demands of the current workforce structure. The OFM provides tremendous value to APPD in terms of time, requiring only seconds to solve rather than months; this enables APPD to conduct quick what-if analyses for decision support, which was impossible to do manually.

Suggested Citation

  • Nathaniel D. Bastian & Pat McMurry & Lawrence V. Fulton & Paul M. Griffin & Shisheng Cui & Thor Hanson & Sharan Srinivas, 2015. "The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions," Interfaces, INFORMS, vol. 45(4), pages 305-324, August.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:4:p:305-324
    DOI: 10.1287/inte.2014.0779
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    References listed on IDEAS

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    Cited by:

    1. Jingbo Huang & Jiting Li & Yonghao Du & Yanjie Song & Jian Wu & Feng Yao & Pei Wang, 2023. "Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    2. Turan, Hasan Hüseyin & Jalalvand, Fatemeh & Elsawah, Sondoss & Ryan, Michael J., 2022. "A joint problem of strategic workforce planning and fleet renewal: With an application in defense," European Journal of Operational Research, Elsevier, vol. 296(2), pages 615-634.
    3. Nathaniel D. Bastian & Tahir Ekin & Hyojung Kang & Paul M. Griffin & Lawrence V. Fulton & Benjamin C. Grannan, 2017. "Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems," Health Care Management Science, Springer, vol. 20(2), pages 246-264, June.
    4. Oussama Mazari-Abdessameud & Filip Van Utterbeeck & Guy Van Acker & Marie-Anne Guerry, 2020. "Multidimensional military manpower planning based on a career path approach," Operations Management Research, Springer, vol. 13(3), pages 249-263, December.
    5. Lee A Evans & Ki-Hwan G Bae, 2019. "US Army performance appraisal policy analysis: a simulation optimization approach," The Journal of Defense Modeling and Simulation, , vol. 16(2), pages 191-205, April.
    6. Günay Uzun & Metin Dağdeviren & Mehmet Kabak, 2016. "Determining the Distribution of Coast Guard Vessels," Interfaces, INFORMS, vol. 46(4), pages 297-314, August.
    7. Bastian, Nathaniel D. & Lunday, Brian J. & Fisher, Christopher B. & Hall, Andrew O., 2020. "Models and methods for workforce planning under uncertainty: Optimizing U.S. Army cyber branch readiness and manning," Omega, Elsevier, vol. 92(C).

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