A JOINT CHANCE-CONSTRAINED PROGRAMMING-MODEL WITH ROW DEPENDENCE

Joint chance-constrained stochastic programming models typically requi re random row vector independence. A joint model is developed that inc orporates not only within-constraint covariance as is usually the case , but also admits dependence between constraints, that is, row depende nce. The objective function of the associated chance-constrained deter ministic equivalent is a multivariate normal distribution with dimensi on 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.