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US Army performance appraisal policy analysis: a simulation optimization approach

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  • 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
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    References listed on IDEAS

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    2. 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.
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    3. 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.

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