in this paper, a model of an optimal control problem with chance constraints is introduced. the parametersof the constraints are fuzzy, random or fuzzy random variables. todefuzzify the constraints, we consider possibility levels.  bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical op...

In this paper, a model of an optimal control problem with chance constraints is introduced. The parametersof the constraints are fuzzy, random or fuzzy random variables. Todefuzzify the constraints, we consider possibility levels.  Bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical op...

Chance constraints provide a principled framework to mitigate the risk of high-impact extreme events by modifying controllable properties system. The low probability and rare occurrence such events, however, impose severe sampling computational requirements on classical solution methods that render them impractical. This work proposes novel sampling-free method for solving chance constrained op...

We consider an optimization problem of minimization of a linear function subject to the chance constraint Prob{G(x, ξ) ∈ C} ≥ 1 − ε, where C is a convex set, G(x, ξ) is bi-affine mapping and ξ is a vector of random perturbations with known distribution. When C is multi-dimensional and ε is small, like 10−6 or 10−10, this problem is, generically, a problem of minimizing under a nonconvex and dif...

We study stochastic variants of flow-based global constraints as combinatorial chance constraints. As a specific case study, we focus on the stochastic weighted alldifferent constraint. We first show that determining the consistency of this constraint is NP-hard. We then show how the combinatorial structure of the alldifferent constraint can be used to define chance-based filtering, and to comp...

In this paper, we study the linear fractional transportation problem with uncertain arameters. After recalling some definitions, concepts and theorems in uncertainty theory we present three approaches for solving this problem. First we consider the expected value of the objective function together with the expectation of satisfying constraints. Optimizing the expected value of the objective fun...

This paper studies an n-player non-cooperative game where each player has expected-value payoff function and chance-constrained strategy set. We consider the case row vectors defining constraints are independent random whose probability distributions not completely known belong to a certain distributional uncertainty The sets defined using distributionally robust framework. one density based se...

In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single (SCCs). Using Boole's inequality or its improved versions decompose JCCs into SCCs is popular, yet the conservativeness introduced still significant. this letter, non-parametric iterative framework proposed achieve decomposition of with negligible conservativeness. An adap...