Tractable Approximation of Dynamic Decision Making Models Under Uncertainty

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The objective of this award is to propose a practical, efficient, robust and scalable modeling framework to a broad class of dynamic decision making models under uncertainty. Convex approximation approaches will be proposed to solve multi-stage stochastic program problems with limited data distributional information. Mechanisms to measure the quality of the resulting approximations, both theoretically and computationally, will be developed. Their relationship with robust optimization methodology will also be explored. The proposed tractable approximation approach will be applied to model and solve a variety of important large scale practical problems ranging from supply chain management, healthcare, financial engineering and water resource management. If successful, this project would greatly enhance our capabilities to modeling dynamic decision making problems under uncertainty and provide computational tools to solving large scale stochastic programming problems. It would allow the application of stochastic programming to large scale practical dynamic decision making models which were previously out of reach due to their computational complexity. The project would also demonstrate the potential of incorporating uncertainty into practical decision making problems faced by enterprises and facilitate firms with models and computational tools to develop much-needed decision support systems that can handle uncertainty efficiently and effectively.