A JOINT CHANCE-CONSTRAINED PROGRAMMING-MODEL WITH ROW DEPENDENCE

Joint chance-constrained stochastic programming models typically require random row vector independence. A joint model is developed that incorporates not only within-constraint covariance as is usually the case, but also admits dependence between constraints, that is, row dependence. The objective function of the associated chance-constrained deterministic equivalent is a multivariate normal distribution with dimension equal to the number of chance constraints in the original problem. We discuss methods to solve this multinormal integral and evaluate its derivatives. The model is implemented in portable Fortran and applied to two 9-D test problems.