Collaborative Research: Inference on expensive, grey-box simulation models
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abstract
This grant will advance the national prosperity and welfare by allowing organizations to use stochastic computer simulation models to make critical strategic, tactical and operational decisions in the face of uncertainty. Such decisions in supply chain logistics, transportation, healthcare, defense planning and finance entail choosing from among millions of scenarios based on estimates of key performance indicators. The award supports fundamental research on a flexible framework for exploiting information about simulated system performance to obtain stronger inference and better decisions for hitherto unsolvable large-scale problems. Close collaboration between academic researchers and industrial practitioners at leading U.S. companies will yield rapid, reliable algorithms that can be tailored to a diverse array of simulation problems across many industries and government agencies. All results will be made available as open-source software and the methods communicated in the form of tutorials and instructional materials for graduate engineering, data science and business classes.
The research is motivated by modern large-scale simulation problems and the shortcomings of state-of-the-art general-purpose methods that treat a simulation model as a “black box.” The work will create methods that efficiently carry out statistical inference by extracting and exploiting additional structural information available from the “grey-box” nature of modern simulations. Specifically, it will leverage this information to strengthen the delivered inferences and offer certifiable guarantees for expensive stochastic simulation models. The methods will account for multiple, conflicting performance measures and exploit structural information to drive the sequential experiment design of simulation runs, as well as verify and extract the needed structural information from a simulation model to obtain scalable computational methods.
