Incorporating learning curve costs in acquisition strategy optimization

Each year, the US Army procures billions of dollars worth of weapons and equipment. The process of deciding what to buy, when to buy, and in what quantities is extremely complex, requiring extensive analysis. Two techniques used in this analysis are mathematical programming and cost estimation. Although they are related through constraints on available procurement funds, the use of nonlinear cost learning curves, which better represent system costs as a function of quantity produced, have not been incorporated into the mathematical programming formulations that compute the quantities of items to be procured. As a result, the solutions obtained could be either suboptimal, or even infeasible with respect to budgetary limitations. In this paper we present a piecewise linear approximation of the learning curve costs for a more accurate portrayal of budgetary constraints used in a mixed integer linear programming for acquisition strategy optimization. In addition, implementation issues are discussed, and performance results are given.