The AMEDD uses goal programming to optimize workforce planning decisions

Nathaniel D. Bastian, Pat McMurry, Lawrence V. Fulton, Paul M. Griffin, Shisheng Cui, Thor Hanson, Sharan Srinivas

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)305-324
Number of pages20
JournalInterfaces
Volume45
Issue number4
DOIs
Publication statusPublished - 1 Jul 2015
Externally publishedYes

Keywords

  • Goal programming
  • Military medicine
  • Mixed-integer linear programming
  • Multiple-criteria decision making
  • Stochastic optimization
  • Workforce planning

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Bastian, N. D., McMurry, P., Fulton, L. V., Griffin, P. M., Cui, S., Hanson, T., & Srinivas, S. (2015). The AMEDD uses goal programming to optimize workforce planning decisions. Interfaces, 45(4), 305-324. https://doi.org/10.1287/inte.2014.0779