IDEAS home Printed from https://ideas.repec.org/a/sae/joudef/v16y2019i2p191-205.html
   My bibliography  Save this article

US Army performance appraisal policy analysis: a simulation optimization approach

Author

Listed:
  • Lee A Evans
  • Ki-Hwan G Bae

Abstract

An effective performance appraisal system is critical in identifying officers with the knowledge, skills, and abilities to lead the future military force. The US Army uses a forced distribution performance appraisal system that limits the number of above average evaluations raters can award to their subordinates. Aside from job performance, multiple factors contribute to the rating an individual receives in such systems. These factors include a rater’s span of control (the number of subordinates being rated), the frequency at which individuals change raters, regulatory constraints pertaining to the number of top evaluations a rater can award, and the rater behavior. Using performance appraisal data provided by the US Army Human Resources Command, we develop a discrete-event simulation model that represents Army officers in the current forced distribution performance appraisal system. We then apply ranking and selection simulation optimization techniques to evaluate and optimize controllable input parameters in the simulated system. Our results show the potential of reducing the number of officers not receiving the number of above average evaluations commensurate with their performance level by as much as 24%. The results also further indicate the general applicability of simulation optimization in the fields of manpower modeling and policy analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:joudef:v:16:y:2019:i:2:p:191-205
    DOI: 10.1177/1548512918787969
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1548512918787969
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1548512918787969?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. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    2. 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.
    3. 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.
    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. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.

    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. 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.
    2. 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.
    3. 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.
    4. Sigrún Andradóttir & Seong‐Hee Kim, 2010. "Fully sequential procedures for comparing constrained systems via simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 403-421, August.
    5. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    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. 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.
    8. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
    9. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    10. Sigrún Andradóttir, 2002. "Simulation Optimization: Integrating Research and Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 216-219, August.
    11. Teng, Suyan & Lee, Loo Hay & Chew, Ek Peng, 2010. "Integration of indifference-zone with multi-objective computing budget allocation," European Journal of Operational Research, Elsevier, vol. 203(2), pages 419-429, June.
    12. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2004. "Selecting the best stochastic system for large scale problems in DEDS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 237-245.
    13. Ying Zhong & Shaoxuan Liu & Jun Luo & L. Jeff Hong, 2022. "Speeding Up Paulson’s Procedure for Large-Scale Problems Using Parallel Computing," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 586-606, January.
    14. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.
    15. Shing Chih Tsai & Wu Hung Lin & Chia Cheng Wu & Shao Jen Weng & Ching Fen Tang, 2022. "Decision support algorithms for optimizing surgery start times considering the performance variation," Health Care Management Science, Springer, vol. 25(2), pages 208-221, June.
    16. Shing Chih Tsai & Tse Yang, 2017. "Rapid screening algorithms for stochastically constrained problems," Annals of Operations Research, Springer, vol. 254(1), pages 425-447, July.
    17. 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.
    18. Tsai, Shing Chih & Chu, I-Hao, 2012. "Controlled multistage selection procedures for comparison with a standard," European Journal of Operational Research, Elsevier, vol. 223(3), pages 709-721.
    19. Chen, E. Jack & Kelton, W. David, 2005. "Sequential selection procedures: Using sample means to improve efficiency," European Journal of Operational Research, Elsevier, vol. 166(1), pages 133-153, October.
    20. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.

    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:16:y:2019:i:2:p:191-205. 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.