A CONSTRAINED OPTIMIZATION PROBLEM UNDER UNCERTAINTY

We investigate a constrained optimization problem for which there is uncertainty about a constraint parameter. Our aim is to reformulate it as a (constrained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models—linear and vacuous previsions, and possibility distributions—and for two different optimality criteria for decision problems under uncertainty—maximinity and maximality.Book subtitle: PROCEEDINGS OF THE 9TH INTERNATIONAL FLINS CONFERENCEstatus: publishe