This study investigates capacity portfolio planning problems under demand, price, and yield uncertainties. We model this capacity portfolio planning problem as a Markov decision process. In this research, we consider two types of capacity: dedicated and flexible capacity. Among these capacity types, flexible capacity costs higher but provides flexibility for producing different products. To maximize expected profit, decision makers have to choose the optimal capacity level and expansion timing for both capacity types. Since large stochastic optimization problems are intractable, a new heuristic search algorithm (HSA) is developed to reduce computational complexity. Compare to other algorithms in literature, HSA reduces computational time by at least 30% in large capacity optimization problems. In addition, HSA yields optimal solution in all numerical examples that we have examined.
['Cheng-Hung Wu', 'Ya-Tang Chuang']
An efficient algorithm for stochastic capacity portfolio planning problems