A mixed-integer conic programming formulation for computing the flexibility index under multivariate gaussian uncertainty. (2nd November 2018) - Pulsipher, Joshua L.; Zavala, Victor M.
Highlights: Present mixed-integer formulation to compute flexibility index under Gaussian uncertainty. Index can be used to obtain conservative approximation of stochastic flexibility. Approach is scalable. Abstract: We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility index by solving a mixed-integer conic program (MICP). This methodology directly characterizes ellipsoidal sets to capture correlations in contrast to previous methodologies that employ approximations. We also show that, under a Gaussian representation, the flexibility index can be used to obtain a lower bound for the so-called stochastic flexibility index (i.e., the probability of having feasible operation). Our results also show that the methodology can be generalized to capture different types of uncertainty sets.