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Optimization-Based Calibration of Simulation Input Models

Aleksandrina Goeva (), Henry Lam (), Huajie Qian () and Bo Zhang ()
Additional contact information
Aleksandrina Goeva: Broad Institute, Cambridge, Massachusetts 02142
Henry Lam: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Huajie Qian: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Bo Zhang: IBM Research AI, Yorktown Heights, New York 10598

Operations Research, 2019, vol. 67, issue 5, 1362-1382

Abstract: Studies on simulation input uncertainty are often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output data, we nonparametrically calibrate the input models and other related performance measures of interest. We propose an optimization-based framework to compute statistically valid bounds on input quantities. The framework utilizes constraints that connect the statistical information of the real-world outputs with the input–output relation via a simulable map. We analyze the statistical guarantees of this approach from the view of data-driven distributionally robust optimization, and show how they relate to the function complexity of the constraints arising in our framework. We investigate an iterative procedure based on a stochastic quadratic penalty method to approximately solve the resulting optimization. We conduct numerical experiments to demonstrate our performances in bounding the input models and related quantities.

Keywords: model calibration; distributionally robust optimization; uncertainty quantification; input modeling (search for similar items in EconPapers)
Date: 2019
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