The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their ‘‘deterministic equivalents.’’ This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the n...

We propose a safe approximation to joint chance-constrained programming, where the constraint functions are additively dependent on normally-distributed random vector. The is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, standard nonlinear program. compare this new another analytical for programming bas...

The problem of optimizing affine feedback laws that explicitly steer the mean and covariance an uncertain system state in presence a Gaussian random field is considered. Spatially-dependent disturbances are successively approximated with respect to nominal trajectory by sequence jointly vectors. Sequential updates control inputs computed via convex optimization includes effect feedback, perturb...

 Recently, Khodabakhshi and Aryavash have introduced a ranking method [Applied Mathematics Letters, 25 (2012) 2066-2070.], which is based on an optimistic-pessimistic approach of data envelopment analysis (DEA). This method ranks all decision making units according to a combination of their minimum and maximum possible efficiency scores which are determined by solving two linear programming mo...