Dynamic Programming (DP) provides a powerful framework for modeling
complex decision problems where uncertainty is resolved and
decisions are made over time. But it is difficult to scale to
complex problems. Monte Carlo simulation methods, however,
typically scale well, but typically do not provide a good way to
identify an optimal policy or provide a performance bound. To
address these restrictions, the authors review the information
relaxation approach which works by reducing a complex stochastic DP
to a series of scenario-specific deterministic optimization
problems solved within a Monte Carlo simulation.Written in a
tutorial style, the authors summarize the key ideas of information
relaxation methods for stochastic DPs and demonstrate their use in
several examples. They provide a "one-stop-shop" for researchers
seeking to learn the key ideas and tools for using information
relaxation methods.This book provides the reader with a
comprehensive overview of a powerful technique for use by students,
researchers and practitioners.
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