A chance-constrained optimisation (CCOPT) model has a dual goal: guaranteeing
performance as well as system reliability under uncertainty. The beginning of CCOPT …

Portfolio optimization involves the optimal assignment of limited capital to different available
financial assets to achieve a reasonable trade-off between profit and risk. We consider an …

Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to
solve. This work proposes a smooth approximation approach consisting of an inner and an …

For many decision-making problems under uncertainty, it is crucial to develop risk-averse
models and specify the decision makers' risk preferences based on multiple stochastic …

Chance constrained optimization problems in engineering applications possess highly
nonlinear process models and non-convex structures. As a result, solving a nonlinear non …

The major objective of this paper is to give concise summary and brief discussions on latest
developments of optimization methods under uncertainty in process systems engineering …

We deal with chance constrained problems with differentiable nonlinear random functions
and discrete distribution. We allow nonconvex functions both in the constraints and in the …

We consider stochastic programs with joint chance constraints with discrete random
distribution. We reformulate the problem by adding auxiliary variables. Since the resulting …

The vast majority of the machine scheduling literature focuses on deterministic problems in
which all data is known with certainty a priori. In practice, this assumption implies that the …

We propose an equivalent reduction of the quantile optimization problem with a discrete
distribution of random parameters to a partially integer programming problem of large …