Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 268, Issue 1, Pages 25-36Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2017.11.003
Keywords
Stochastic programming; Large scale optimization; Complementarity problems; Approximation methods
Funding
- NSF [1745375]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1745375] Funding Source: National Science Foundation
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [1559339] Funding Source: National Science Foundation
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We provide an efficient method to approximate the covariance between decision variables and uncertain parameters in solutions to a general class of stochastic nonlinear complementarity problems. We also develop a sensitivity metric to quantify uncertainty propagation by determining the change in the variance of the output due to a change in the variance of an input parameter. The covariance matrix of the solution variables quantifies the uncertainty in the output and pairs correlated variables and parameters. The sensitivity metric helps in identifying the parameters that cause maximum fluctuations in the output. The method developed in this paper optimizes the use of gradients and matrix multiplications which makes it particularly useful for large-scale problems. Having developed this method, we extend the deterministic version of the North American Natural Gas Model (NANGAM), to incorporate effects due to uncertainty in the parameters of the demand function, supply function, infrastructure costs, and investment costs. We then use the sensitivity metrics to identify the parameters that impact the equilibrium the most. (C) 2017 Elsevier B.V. All rights reserved.
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