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 () and Sharan Srinivas ()
Additional contact information
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

Interfaces, 2015, vol. 45, issue 4, 305-324

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.

Keywords: workforce planning; mixed-integer linear programming; stochastic optimization; goal programming; multiple-criteria decision making; military medicine (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://dx.doi.org/10.1287/inte.2014.0779 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:45:y:2015:i:4:p:305-324

Access Statistics for this article

More articles in Interfaces from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().