Developing Discrete Empirical Distributions for Tractable Stochastic Programming Problems with Application for U.S. Army Force Sizing

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dc.contributor.advisorBerg, Bjorn
dc.contributor.authorChecco, John
dc.creatorChecco, John
dc.date.accessioned2015-09-14T14:22:00Z
dc.date.available2015-09-14T14:22:00Z
dc.date.issued2015
dc.description.abstractProviding for the common defense of the United States and its interests is a vital, though expensive, endeavor. To that end, the U.S. Army is expected to grow and shrink as necessary to conform to changing circumstances. Current analytic methods focus on operational (combat) requirements in developing resource constrained operational force designs. These tend to rely on simulation of static operational force structures with fixed readiness policies. Additionally, the Army’s primary strategic force structure analysis models have no direct linkage to cost, instead relying on manpower limits. This type of approach is ill-suited to consider the cost vs. benefit tradeoff of the Army, or the overall size and composition of the Army, and how it should adapt over time to changing conditions.
dc.format.extent337 pages
dc.identifier.urihttps://hdl.handle.net/1920/9880
dc.language.isoen
dc.rightsCopyright 2015 John Checco
dc.subjectOperations research
dc.subjectArmy force size
dc.subjectArmy force structure
dc.subjectDiscrete Empirical Distribution
dc.subjectMultistage
dc.subjectStochastic Optimization
dc.subjectStochastic Programming
dc.titleDeveloping Discrete Empirical Distributions for Tractable Stochastic Programming Problems with Application for U.S. Army Force Sizing
dc.typeDissertation
thesis.degree.disciplineSystems Engineering
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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