| Summary: | This thesis is the second part of a three-part thesis study that was started by LTC Martin Fair in June 2004. In his initial thesis, LTC Fair built a database by joining information from the U.S. Census Bureau, U.S. zip codes, and USAR zip code data. LTC Fair also formulated a network flow model and began an initial implementation of the first of many constraints. My thesis will validate the constraint models and develop the set of constraints that another project, by LTC Brau, will need to develop the network flow model. That model will optimize reserve unit readiness in the third and perhaps final part of the study. Since the early 1990's and the demise of the Cold War, the United States Army active and reserve forces have undergone dramatic restructuring. The Active component was reduced in size from 18 active divisions down to today's total of ten-a force cut of approximately 300,000 soldiers. Additionally, the United States Army Reserve forces mission shifted to a predominately Combat Support (CS) and Combat Service Support (CSS) mission. This realignment was an attempt to use the USAR component in a support role as the world situation dictated. Since the terrorist attacks of September 11, 2001, and the subsequent declaration of a "War on Terrorism," the United States Army Reserve (and active component) has been called upon to deploy more frequently and for extended periods of time. Maintaining unit readiness and a satisfactory "fill-rate" is probably one of the leading challenges that our reserve forces face. This thesis examines the relationship between unit location and recruiting success. We seek to maximize the fill rate of United States Army Reserve (USAR) units. Our method will correlate the vocational aptitudes of the US population with the Military Occupational Specialties (MOS) of the USAR units.
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