A Joint Chance Constrained Programming Model with Row Dependence

Tsunemi Watanabe, Hugh Ellis*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

33 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)325-343
Number of pages19
JournalEuropean Journal of Operational Research
Issue number2
Publication statusPublished - 8 Sept 1994
Externally publishedYes


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