This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain...

A blind adaptive multiuser detector (MUD) based on the Linearly Constrained Constant Modulus Algorithm is presented. The dependence of the performance (in terms of BER and SINR) on step size and amplitude estimation is quantitatively investigated through computer simulation.

In this paper, we deal with fuzzy random variables for inputs andoutputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzyrandom flat LR numbers with known distribution. The problem is to find a method forconverting the imprecise chance-constrained DEA model into a crisp one. This can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

Three types of fuzzy random programming for capacitated location-allocation problem with fuzzy random demands are proposed according to different criteria, including the expected value model, chanceconstrained programming and dependent-chance programming. Finally, integrating the network simplex algorithm, fuzzy random simulation and genetic algorithm, a hybrid intelligent algorithm is produced...

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...