Models and Methods for Workforce Planning Under Uncertainty: Optimizing U.S. Army Cyber Branch Readiness and Manning
USMA Research Unit Affiliation
Army Cyber Institute, Mathematical Sciences, Operations Research Center, Systems Engineering
This work examines the problem of optimally resourcing personnel for a new set of U.S. Army cyber-specialty career fields using a combination of personnel accessions and inter-career-field transfers that are limited to occur over a subset of periods early within a 30-year career cycle. This workforce planning problem is bounded by constraints respectively pertaining to organizational needs for personnel, as well as personnel promotion policies. Complicating the problem are stochastic retention rates for every combination of year and promotion level within each career field over the 30-year period. Upon formalizing this Cyber Workforce Planning Problem (CWPP) within the framework of multiple performance goals, this study formulates, tests, and compares three frameworks to seek optimal workforce planning decisions under uncertainty: two stochastic programming (SP) variants and a robust optimization (RO) representation. Upon sampling the stochastic parametric distributions to generate a set of collectively representative, deterministic scenarios, the SP variants respectively leverage a scenario-based Monte Carlo approach and sample average approximation, whereas the RO model examines robust solutions over a range of decision-maker risk attitudes. A sensitivity analysis applied to the first of the SP variants indicates the solutions are relatively insensitive to different prioritizations of goals, and identifies policy insights to help recruit and retain personnel. Comparative testing of the three methodologies yields workforce planning recommendations that are relatively consistent across solution methodologies and identify concerns to inform changes to personnel policies.
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