Stochastic preemptive goal programming of Air Force weapon systems mix. (April 2023)

• Record Type: Journal Article
• Authors: Anderson, Justin L; Astudillo, Jessica M; Butcher, Zachary E; Cornman, Matthew D; Correale, Anthony J; Crumpacker, James B; Dennie, Nathaniel C; Gaines, Alex R; Gallagher, Mark A; Goodwill, John C; Graves, Emily S; Hale, Donald B; Holland, Kimberly G; Huffman, Benjamin D; McGee, Michelle; Pollack, Nicholas A; Ramirez, Rachel C; Song, Camero; Swize, Emmie K; Tello, Erick A; Wales, Jesse G; Walker, Julius C; Wilson, Andrew B; Wilson, William F; Wooten, Kylie E; Zawadzki, Marcelo
• Is Part Of: Journal of defense modeling and simulation. Volume 20:Number 2(2023)
• Page Start: 147
• Page End: 158
• Publication Date: 2023-04
• Subjects: Stochastic -- goal programming -- uncertainty -- risk -- military forces -- weapon systems; Military art and science -- Computer simulation -- Periodicals; 355.0011305
• DOI: 10.1177/15485129211051751
• Languages: English
• ISSNs: 1548-5129

Abstract

We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for military force effectiveness and the force's cost. We use the Bayesian Enterprise Analytic Model (BEAM), as described in "Probabilistic Analysis of Complex Combat Scenarios, " to evaluate effectiveness, expressed in terms of the probability of achieving campaign objectives, in three hypothetical scenarios. We develop cost estimates along with their uncertainty to evaluate the force's research and development, production, and annual operating and support costs. Our summary depicts how the trade-off between various prioritized goals influences the recommended robust force. Our approach enables defense leaders to balance risk in both force effectiveness in various scenarios along with risk in different types of cost categories.