Manpower modeling in the airborne community of the United States Army

Abstract

With the evolution of new sophisticated technology, military manpower planning problems involving specialized training have become increasingly important. This problem has become especially urgent for the airborne community of the U.S. Army because this community has a variety of special training requirements which make the problem especially complex when other dimensions such as grade level and military occupation specialty are also taken into account. This thesis formulates a methodology which applies Markov chain theory to forecast future inventories and uses marginal analysis to determine the optimal numbers of soldiers with certain skill levels and job types who should enter special training. The goal of the optimization model is the minimization of the maximum percentage shortage of personnel relative to authorization. The methodology is used with FY 1984 data to determine the optimal numbers of soldiers to enter special training during fiscal years1984 and 1985 to minimize the maximum percent shortage.