Optimal investment in product-flexible manufacturing capacity
Management Science
Capacity expansion under stochastic demands
Operations Research - Supplement to Operations Research: stochastic processes
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Coordinating Strategic Capacity Planning in the Semiconductor Industry
Operations Research
Capacity sharing in a network of independent factories: A cooperative game theory approach
Robotics and Computer-Integrated Manufacturing
Stochastic lot-sizing with backlogging: computational complexity analysis
Journal of Global Optimization
Designing decision support systems for value-based management: A survey and an architecture
Decision Support Systems
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
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This paper addresses a general class of capacity planning problems under uncertainty, which arises, for example, in semiconductor tool purchase planning. Using a scenario tree to model the evolution of the uncertainties, we develop a multistage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multistage model allows for revision of the capacity expansion plan as more information regarding the uncertainties is revealed. We provide analytical bounds for the value of multistage stochastic programming (VMS) afforded over the two-stage approach. By exploiting a special substructure inherent in the problem, we develop an efficient approximation scheme for the difficult multistage stochastic integer program and prove that the proposed scheme is asymptotically optimal. Computational experiments with realistic-scale problem instances suggest that the VMS for this class of problems is quite high; moreover, the quality and performance of the approximation scheme is very satisfactory. Fortunately, this is more so for instances for which the VMS is high.