A scenario tree approach to multi-period project selection problem using real-option valuation method

Abstract

Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Multi-period project portfolio selection problems can be modeled by multistage stochastic programs. These models utilize a set of scenarios and corresponding probabilities to model the multivariate random data process (costs or revenues, available budget, chance of success). For most practical problems, the optimization problem that contains all possible scenarios is too large. Due to computational complexity, this program is often approximated by a model involving a (much) smaller number of scenarios. The scenario reduction algorithms determine a subset of the initial scenario set and assign new probabilities to the preserved scenarios such that optimal values and approximate solution sets remain close to each other. This paper presents a stochastic optimization model of pharmaceutical multi-period project selection problem using a real-option valuation method. Subsequently, it reduces scenarios via existing algorithms to generate a new (smaller) scenario tree. Model with reduced scenarios has been solved in this research. Numerical results indicate improved portfolios and show the effectiveness of the proposed algorithm.