An innovative approach for strategic capacity portfolio planning under uncertainties

This research studies multi-generation capacity portfolio planning problems under various uncertainty factors. These uncertainty factors include price uncertainties, demand fluctuation and uncertain product life cycle. The objective of this research is to develop an efficient algorithm that generates capacity portfolio policies robust to aforementioned uncertainties. We model this capacity portfolio planning problem using Markov decision processes (MDP). In this MDP model, we consider two generation of manufacturing technology. The new generation capacity serves as a flexible resource that can be used to downward fulfill the deficiency of old generation capacity. The objective of this MDP model is to maximize the expected profit under uncertainties. An efficient algorithm is proposed to solve the problem and provide an optimal capacity expansion policy for both types of capacity. Moreover, we show that the optimal capacity expansion policy can be characterized by a monotone structure. We verify our results by detail simulation study.