Optimizing US army force size under uncertainty through stochastic programming

John Checco, Bjorn Berg, Andrew Loerch

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Military pensive, half of all forces US accounting discretionary are inherently for spend- about ex-ing (Obama, 2014). The US Army, which is manpower-centric, is expected to expand and contract as necessary to meet changing conditions more than the other armed forces. Current analytic methods do not adequately address the appropriate size and composition of the total Army force nor how it should be adjusted over time. This research provides the means to analyze how the Army workforce should change over time while accounting for different personnel types, cost, institutional and operational forces, and the risk of failing to meet deployment requirements. We present a multistage stochastic programming model of Army manpower requirements including a discrete probability distribution of deployment demand derived from stochastic simulation and novel constraints to model the cyclic multiyear defense planning process. Model results are used to develop an objective cost-risk relationship of Army force size.

Original languageEnglish (US)
Pages (from-to)19-37
Number of pages19
JournalMilitary Operations Research
Issue number2
StatePublished - Jan 1 2017


Dive into the research topics of 'Optimizing US army force size under uncertainty through stochastic programming'. Together they form a unique fingerprint.

Cite this