Retention Prediction and Policy Optimization for United States Air Force Personnel Management
Abstract
Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets. To improve retention predictions, we develop and test traditional machine learning models as well as partially autoregressive forms, outperforming the benchmark on a test dataset by 62.8 and 34.8 for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted personnel, then extend this formulation to include the Air Reserve Components. We develop and test an adaptation of Concave Adaptive Value Estimation (CAVE) on the active duty problem, finding that CAVE reduces costs from the benchmark policy by 29.76 and 17.38 for the two cost functions tested. We test CAVE across a range of hyperparameters for the larger intercomponent problem, reducing costs by 23.06 from the benchmark, then develop the Stochastic Use of Perturbations to Enhance Robustness of CAVE (SUPERCAVE) algorithm, reducing costs by another 0.67. Resulting algorithms and methods are directly applicable to contemporary USAF personnel business practices, enabling more accurate, less time-intensive, and data-informed policy targets for current processes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 19, 2022
- Accession Number
- AD1181265
Entities
People
- Joseph C. Hoecherl
Organizations
- Air Force Institute of Technology