IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v45y2015i4p305-324.html
   My bibliography  Save this article

The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2014.0779
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2014.0779?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. E. S. Bres & D. Burns & A. Charnes & W. W. Cooper, 1980. "A Goal Programming Model for Planning Officer Accessions," Management Science, INFORMS, vol. 26(8), pages 773-783, August.
    2. Dan Shrimpton & Alexandra M. Newman, 2005. "The US Army Uses a Network Optimization Model to Designate Career Fields for Officers," Interfaces, INFORMS, vol. 35(3), pages 230-237, June.
    3. Silverman, Joe & Steuer, Ralph E. & Whisman, Alan W., 1988. "A multi-period, multiple criteria optimization system for manpower planning," European Journal of Operational Research, Elsevier, vol. 34(2), pages 160-170, March.
    4. Rubinstein, Reuven Y. & Shapiro, Alexander, 1990. "Optimization of static simulation models by the score function method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(4), pages 373-392.
    5. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    6. Saul I. Gass & Roger W. Collins & Craig W. Meinhardt & Douglas M. Lemon & Marcia D. Gillette, 1988. "OR Practice—The Army Manpower Long-Range Planning System," Operations Research, INFORMS, vol. 36(1), pages 5-17, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. Günay Uzun & Metin Dağdeviren & Mehmet Kabak, 2016. "Determining the Distribution of Coast Guard Vessels," Interfaces, INFORMS, vol. 46(4), pages 297-314, August.
    6. 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).
    7. 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.

    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. Sharan Srinivas & Mohammadmahdi Alizadeh & Nathaniel D.Bastian, 2017. "Optimizing Student Team and Job Assignments for the Holy Family Academy," Interfaces, INFORMS, vol. 47(2), pages 163-174, April.
    2. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    3. 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).
    4. Yongjian Li & Jian Chen & Xiaoqiang Cai, 2007. "An integrated staff-sizing approach considering feasibility of scheduling decision," Annals of Operations Research, Springer, vol. 155(1), pages 361-390, November.
    5. 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.
    6. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    7. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    8. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    9. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    10. Hermann Held, 2019. "Cost Risk Analysis: Dynamically Consistent Decision-Making under Climate Targets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 247-261, January.
    11. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    12. Wu, Desheng (Dash) & Lee, Chi-Guhn, 2010. "Stochastic DEA with ordinal data applied to a multi-attribute pricing problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1679-1688, December.
    13. Odetayo, Babatunde & MacCormack, John & Rosehart, William D. & Zareipour, Hamidreza, 2017. "A sequential planning approach for Distributed generation and natural gas networks," Energy, Elsevier, vol. 127(C), pages 428-437.
    14. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    15. Ghazale Kordi & Parsa Hasanzadeh-Moghimi & Mohammad Mahdi Paydar & Ebrahim Asadi-Gangraj, 2023. "A multi-objective location-routing model for dental waste considering environmental factors," Annals of Operations Research, Springer, vol. 328(1), pages 755-792, September.
    16. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    17. José Fernández & Boglárka Tóth, 2009. "Obtaining the efficient set of nonlinear biobjective optimization problems via interval branch-and-bound methods," Computational Optimization and Applications, Springer, vol. 42(3), pages 393-419, April.
    18. Giada Spaccapanico Proietti & Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2024. "Chance-Constrained Automated Test Assembly," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 92-120, February.
    19. Jana, R.K. & Sharma, Dinesh K. & Chakraborty, B., 2016. "A hybrid probabilistic fuzzy goal programming approach for agricultural decision-making," International Journal of Production Economics, Elsevier, vol. 173(C), pages 134-141.
    20. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

    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:inm:orinte:v:45:y:2015:i:4:p:305-324. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.