Calibrating simulation models using the knowledge gradient with continuous parameters

Authors:
Warren R. Scott;Warren B. Powell;Hugo P. Simão
Affiliations:
Princeton University, Princeton, NJ;Princeton University, Princeton, NJ;Princeton University, Princeton, NJ
Venue:
Proceedings of the Winter Simulation Conference
Year:
2010

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Abstract

We describe an adaptation of the knowledge gradient, originally developed for discrete ranking and selection problems, to the problem of calibrating continuous parameters for the purpose of tuning a simulator. The knowledge gradient for continuous parameters uses a continuous approximation of the expected value of a single measurement to guide the choice of where to collect information next. We show how to find the parameter setting that maximizes the expected value of a measurement by optimizing a continuous but nonconcave surface. We compare the method to sequential kriging for a series of test surfaces, and then demonstrate its performance in the calibration of an expensive industrial simulator.