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Many common decision-making situations require integration of a number of objectives or goals into a single objective function to be optimized. A number of techniques for performing this analysis are presented in the literature. These procedures generally require a tradeoff between realism and tractability. The present approach uses the idea of preemptive priority levels with an approximate value function at each level. An efficient experimental design is used to assess directly a quadratic approximation of the multiattribute value functions. An existing algorithm is then used to solve the resulting problem. This procedure is shown to give good results when compared to both the value-function method and goal programming.

This article examines the influence of both production rate and learning on airframe program costs. A dynamic multiple-output production model is developed and is used to observe the cost impact of changes in production rate and learning. Several simulations are performed to demonstrate the sensitivity of the optimal time path of cost to changes in the model parameters. The model is applied by estimating parameters from the F102 airframe program.