Stochastic model of resource allocation to R & D activities under cost value uncertainty

Abstract

The paper is concerned with the development of a stochastic mathematical model for management of large-scale Research and Development program. The problem of optimal funding of an R & D complex program, consisting of several projects, their components and possible technical approaches is considered. It is assumed that the values of costs of technical approaches as well as the probabilities of technical succes are not known with certainty. So it is advantageous to perform a limited number of diagnostic experiments in order to reduce this uncertainty. The problem is to develop a policy for performing experiments and allocating resources on the basis of the results of the experiments. This policy is such that a chosen performance index is optimized. A computionally practical algorithm for obtaining an approximate solution using the basic stochastic dynamic programming approach is developed. This algorithm preserves the "closed loop" feature of the dynamic programming solution in that the resulting decision policy depends both on the results of past experiments and on the statistics of the outcomes of future experiments. In other words, the present decision takes into account the value of future information.