Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Future Capacity Procurements Under Unknown Demand and Increasing Costs
Management Science
Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments
Manufacturing & Service Operations Management
Salesforce Incentives, Market Information, and Production/Inventory Planning
Management Science
Resource Flexibility with Responsive Pricing
Operations Research
A capacity allocation and expansion model for TFT-LCD multi-site manufacturing
Journal of Intelligent Manufacturing
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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.