Dynamic time-staged model for R&D portfolio planning — A real world case

A dynamic, time-staged mixed integer model is currently being used for the selection of industrial long-range R&D portfolio. Input data from research and marketing are processed through logical programs to provide the discounted payoffs, probabilities (Bayesian, parallel strategies), subjective nonmonetary multiple criteria weights, and the matrix for the mathematical programming model. The multiperiod portfolio is revised sequentially for resources exhaustion throughout the planning horizon by the staged introduction of additional projects. Marginal payoff function (additional investments vs. expected payoff) is a by-product for the support of additional resources justification. Mathematical and heuristic techniques used to overcome common difficulties confronted by previously discussed models are presented. Experience with model introduction to management is also discussed.